With the advancement of vision-language models, image captioning has made significant progress, leading to the generation of more accurate and detailed descriptions. Current image captioning primarily focuses on describing the apparent visual characteristics, which are easily observed by most humans, but less helpful in real-world scenarios. When users seek a deeper understanding of visual content, they may be concerned with fine-grained categories, function properties, and other background knowledge, rather than merely appearances. Additionally, as users' interests vary, there is a growing demand for customizable content generation. To address these challenges, we propose the task of image narrative generation, which aims to produce knowledge-rich natural language responses for input images, customized to the user preference. Furthermore, we propose T^4, an image narrative generation model progressing through cascade steps: Tailor, reTrieve, Think, and Tell. Specifically, it takes the image and various types of prompts as input, and first refines or predicts potentially interesting queries that are tailored to the user expertise level. Subsequently, the model enriches contextual knowledge through retrieval-augmentation and employs chain-of-thoughts to decompose the generation process step by step, thereby telling an accurate and logically coherent image narrative. In addition, we construct the ImgNarr-23K dataset to support task training and evaluation. Experimental results demonstrate that the proposed approach generates image narratives that better satisfy user requirements, and achieves state-of-the-art performance in knowledge-based VQA tasks without additional finetuning. T^4 presents a promising solution for customized content generation in specialized domains.

Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or verifiable world knowledge (factuality).Existing benchmarks provide limited coverage of factuality hallucinations and predominantly evaluate models only in clean settings.We introduce INFACT, a diagnostic benchmark comprising 9,800 QA instances with fine-grained taxonomies for faithfulness and factuality, spanning real and synthetic videos.INFACT evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items.Reliability under induced modes is quantified using Resist Rate (RR) and Temporal Sensitivity Score (TSS).Experiments on 14 representative Video-LLMs reveal that higher Base-mode accuracy does not reliably translate to higher reliability in the induced modes, with evidence corruption reducing stability and temporal intervention yielding the largest degradation.Notably, many open-source baselines exhibit nearzero TSS on factuality, indicating pronounced temporal inertia on order-sensitive questions.

Multilingual capability is a crucial requirement for large multimodal models, which are increasingly deployed across diverse countries and languages. However, most existing benchmarks for multilingual multimodal reasoning fail to effectively distinguish models of different strengths; in fact, even text-only language models without visual capabilities can often achieve high scores. As a result, the comprehensive evaluation of state-of-the-art multilingual multimodal models remains underexplored. In this work, we present M4U, a novel and challenging benchmark designed to evaluate multilingual, multi-discipline multimodal understanding and reasoning. M4U comprises 10k samples spanning 64 disciplines across 16 subfields in Science, Engineering, and Healthcare, covering six languages. Using this benchmark, we conduct extensive evaluations of leading Large Multimodal Models (LMMs) and Large Language Models (LLMs) augmented with external tools. Our results reveal that even the strongest LMMs exhibit pronounced language preferences and struggle with reasoning tasks that require integrating multilingual information across visual and textual modalities. In particular, performance drops markedly when models are prompted with cross-lingual multimodal questions, highlighting significant gaps in current multilingual multimodal reasoning capabilities.1

It is prevalent to leverage unlabeled data to train deep learning models when it is difficult to collect large-scale annotated datasets. However, for 3D gaze estimation, most existing unsupervised learning methods face challenges in distinguishing subtle gaze-relevant information from dominant gaze-irrelevant information. To address this issue, we propose an unsupervised learning framework to disentangle the gaze-relevant and the gaze-irrelevant information, by seeking the shared information of a pair of input images with the same gaze and with the same eye respectively. Specifically, given two images, the framework finds their shared information by first encoding the images into two latent features via two encoders and then switching part of the features before feeding them to the decoders for image reconstruction. We theoretically prove that the proposed framework is able to encode different information into different parts of the latent feature if we properly select the training image pairs and their shared information. Based on the framework, we derive Cross-Encoder and Cross-Encoder++ to learn gaze representation from the eye images and face images, respectively. Experiments on public gaze datasets demonstrate that the Cross-Encoder and Cross-Encoder++ outperform the competitive methods. The ablation study quantitatively and qualitatively shows that the gaze feature is successfully extracted.

Visual recognition models have achieved unprecedented success in various tasks. While researchers aim to understand the underlying mechanisms of these models, the growing demand for deployment in safety-critical areas like autonomous driving and medical diagnostics has accelerated the development of eXplainable AI (XAI). Distinct from generic XAI, visual recognition XAI is positioned at the intersection of vision and language, which represent the two most fundamental human modalities and form the cornerstones of multimodal intelligence. This paper provides a systematic survey of XAI in visual recognition by establishing a multi-dimensional taxonomy from a human-centered perspective based on intent, object, presentation, and methodology. Beyond categorization, we summarize critical evaluation desiderata and metrics, conducting an extensive qualitative assessment across different categories and demonstrating quantitative benchmarks within specific dimensions. Furthermore, we explore the interpretability of Multimodal Large Language Models and practical applications, identifying emerging trends and opportunities. By synthesizing these diverse perspectives, this survey provides an insightful roadmap to inspire future research on the interpretability of visual recognition models.

The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a comprehensive benchmark designed to evaluate biases in LVLMs. VLBiasBench features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race × gender and race × social economic status. To build a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with various questions to create 128,342 samples. These questions are divided into open-ended and close-ended types, ensuring thorough consideration of bias sources and a comprehensive evaluation of LVLM biases from multiple perspectives. We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models.

Recent studies have revealed that text-to-image diffusion models are vulnerable to backdoor attacks, where attackers implant stealthy textual triggers to manipulate model outputs. Previous backdoor detection methods primarily focus on the static features of backdoor samples. However, a vital property of diffusion models is their inherent dynamism. This study introduces a novel backdoor detection perspective named Dynamic Attention Analysis (DAA), showing that these dynamic characteristics serve as better indicators for backdoor detection. Specifically, by examining the dynamic evolution of cross-attention maps, we observe that backdoor samples exhibit distinct feature evolution patterns at the $< $ $> token compared to benign samples. To quantify these dynamic anomalies, we first introduce DAA-I, which treats the tokens' attention maps as spatially independent and measures dynamic feature using the Frobenius norm. Furthermore, to better capture the interactions between attention maps and refine the feature, we propose a dynamical system-based approach, referred to as DAA-S. This model formulates the spatial correlations among attention maps using a graph-based state equation and we theoretically analyze the global asymptotic stability of this method. Extensive experiments across six representative backdoor attack scenarios demonstrate that our approach significantly surpasses existing detection methods, achieving an average F1 Score of 79.27% and an AUC of 86.27%.

Recent approaches employing imperceptible perturbations in input images have demonstrated promising potential to counter malicious manipulations in diffusion-based image editing systems. However, existing methods suffer from limited transferability in cross-model evaluations. To address this, we propose Transferable Defense Against Malicious Image Edits (TDAE), a novel bimodal framework that enhances image immunity against malicious edits through coordinated image-text optimization. Specifically, at the visual defense level, we introduce FlatGrad Defense Mechanism (FDM), which incorporates gradient regularization into the adversarial objective. By explicitly steering the perturbations toward flat minima, FDM amplifies immune robustness against unseen editing models. For textual enhancement protection, we propose an adversarial optimization paradigm named Dynamic Prompt Defense (DPD), which periodically refines text embeddings to align the editing outcomes of immunized images with those of the original images, then updates the images under optimized embeddings. Through iterative adversarial updates to diverse embeddings, DPD enforces the generation of immunized images that seek a broader set of immunity-enhancing features, thereby achieving cross-model transferability. Extensive experimental results demonstrate that our TDAE achieves state-of-the-art performance in mitigating malicious edits under both intra- and cross-model evaluations.

The visual pathway of human brain includes two sub-pathways,i.e., the ventral pathway and the dorsal pathway, which focus on object identification and dynamic information modeling, respectively. Both pathways comprise multi-layer structures, with each layer responsible for processing different aspects of visual information. Inspired by the human visual information processing mechanism, we propose the Brain Inspired Masked Modeling (BIMM) framework, aiming to learn comprehensive representations from videos. Specifically, our approach consists of ventral and dorsal branches, which learn image and video representations, respectively. Both branches employ the Vision Transformer (ViT) as their backbone and are trained through a masked modeling method. To emulate the distinct functions of the visual cortices, we segment the encoder of each branch into three intermediate blocks and reconstruct progressive prediction targets with light weight decoders. Furthermore, drawing inspiration from the information-sharing mechanism in the brain’s visual pathways, we introduce a partial parameter sharing strategy between the branches during training. Extensive experiments demonstrate that BIMM achieves superior performance compared to the state-of-the-art methods.

Recent progress in text-to-image diffusion models has transformed image editing via text prompts, yet this also introduces significant ethical challenges from potential misuse in creating deceptive or harmful content. While current defenses seek to mitigate this risk by embedding imperceptible perturbations, their effectiveness is limited against malicious tampering. To address this issue, we propose a Dual Attention-Guided Noise Perturbation (DANP) immunization method that adds imperceptible perturbations to disrupt the model’s semantic understanding and generation process. DANP functions over multiple timesteps to manipulate both cross-attention maps and the noise prediction process, using a dynamic threshold to generate masks that identify text-relevant and irrelevant regions. It then reduces attention in relevant areas while increasing it in irrelevant ones, thereby misguides the edit towards incorrect regions and preserves the intended targets. Additionally, our method maximizes the discrepancy between the injected noise and the model’s predicted noise to further interfere with the generation. By targeting both attention and noise prediction mechanisms, DANP exhibits impressive immunity against malicious edits, and extensive experiments confirm that our method achieves state-of-the-art performance.

Backdoor attacks targeting text-to-image diffusion models have advanced rapidly. However, current backdoor samples often exhibit two key abnormalities compared to benign samples: 1) Semantic Consistency, where backdoor prompts tend to generate images with similar semantic content even with significant textual variations to the prompts; 2) Attention Consistency, where the trigger induces consistent structural responses in the crossattention maps. These consistencies leave detectable traces for defenders, making backdoors easier to identify. In this paper, toward stealthy backdoor samples, we propose Trigger without Trace (TwT) by explicitly mitigating these consistencies. Specifically, our approach leverages syntactic structures as backdoor triggers to amplify the sensitivity to textual variations, effectively breaking down the semantic consistency. Besides, a regularization method based on Kernel Maximum Mean Discrepancy (KMMD) is proposed to align the distribution of cross-attention responses between backdoor and benign samples, thereby disrupting attention consistency. Extensive experiments demonstrate that our method achieves a 97.5% attack success rate while exhibiting stronger resistance to defenses. It achieves an average of over 98% backdoor samples bypassing three state-of-the-art detection mechanisms, revealing the vulnerabilities of current backdoor defense methods. The code is available at https://github.com/Robin-WZQ/TwT.

Surgical image segmentation is vital for various stages of surgical procedures, from preoperative planning to real-time navigation and postoperative assessment. Despite advances in deep learning, current surgical image segmentation methods remain limited. They primarily target instrument segmentation and show poor generalizability across different surgical settings. While the Segment Anything Model (SAM) shows robust generalization capabilities in the segmentation of natural images, adapting SAM to surgical and medical images faces challenges because of its reliance on high-quality user-provided prompts and inherent lack of design for multi-class semantic segmentation. To address these limitations, we propose Distillation-SAM, an effective method that adapts SAM for accurate surgical image segmentation without user-provided prompts while freezing its encoder and decoder. Distillation-SAM introduces a trainable adapter branch that learns both sparse auto-prompt embeddings and enriched image features with dense auto-prompt embeddings, enabling the segmentation of surgical objects such as vessels, instruments, and tissues. We propose a direct knowledge distillation constraint for these auto-prompt embedding learnings by using embeddings derived from ground-truth masks as guidance. To enable multi-class semantic segmentation using SAM, we revise the mask score regression branch in SAM's decoder by incorporating a trainable Multilayer Perceptron to predict mask categories while keeping other parameters frozen. Our experiments in multiple surgical datasets, including IVIS, EndoVis2017, and Cholecseg8k, demonstrate that distillation-SAM outperforms existing methods in vessel, tissue, and instrument segmentation.

Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for downstream tasks. However, images inherently contain abundant redundant information, leading the pixel-based MIM reconstruction process to focus excessively on finer details such as textures, thus prolonging training times unnecessarily. Addressing this challenge requires a shift towards a compact representation of features during MIM reconstruction. Frequency domain analysis provides a promising avenue for achieving compact image feature representation. In contrast to the commonly used Fourier transform, wavelet transform not only offers frequency information but also preserves spatial characteristics and multi-level features of the image. Additionally, the multi-level decomposition process of wavelet transformation aligns well with the hierarchical architecture of modern neural networks. In this study, we leverage wavelet transform as a tool for efficient representation learning to expedite the training process of MIM. Specifically, we conduct multi-level decomposition of images using wavelet transform, utilizing wavelet coefficients from different levels to construct distinct reconstruction targets representing various frequencies and scales. These reconstruction targets are then integrated into the MIM process, with adjustable weights assigned to prioritize the most crucial information. Extensive experiments demonstrate that our method achieves comparable or superior performance across various downstream tasks while exhibiting higher training efficiency.

This paper explores using large language models (LLMs) as low-level action planners for embodied tasks. While LLMs excel as the robot’s “brain” for high-level planning, they face challenges in directly controlling the “body” by generating precise low-level actions. This limitation arises from LLMs’ strength in high-level conceptual understanding but their inability to handle spatial perception effectively, restricting their potential in embodied tasks. To address this, we bridge the gap by enabling LLMs to not only comprehend complex instructions but also produce actionable, low-level plans. We introduce Room to Chessboard (R2C), a novel semantic representation that maps environmental states onto a grid-based chessboard, empowering LLMs to generate specific low-level coordinates and guide the robot in a manner akin to playing a game of chess. To further enhance decision-making, we propose the Chain-of-Thought Decision (CoT-D) paradigm, which improves LLMs’ interpretability and context-awareness in spatial reasoning. By jointly training LLMs for high-level task decomposition and low-level action generation, we create a unified "brain-body" system capable of handling complex, free-form instructions while producing precise low-level actions, allowing the robot to flexibly control its movements and adapt to varying tasks. We validate R2C using both fine-tuned open-source LLMs and GPT-4, demonstrating effectiveness on the challenging ALFRED benchmark. Results show that with our R2C framework, LLMs can effectively act as low-level planners, generalizing across diverse settings and open-vocabulary robotic tasks. The code and demonstrations are available at: https://vipl-vsu.github.io/Room2Chessboard.

Human pose plays a crucial role in the digital age. While recent works have achieved impressive progress in understanding and generating human poses, they often support only a single modality of control signals and operate in isolation, limiting their application in real-world scenarios. This paper presents UniPose, a framework employing Large Language Models (LLMs) to comprehend, generate, and edit human poses across various modalities, including images, text, and 3D SMPL poses. Specifically, we apply a pose tokenizer to convert 3D poses into discrete pose tokens, enabling seamless integration into the LLM within a unified vocabulary. To further enhance the fine-grained pose perception capabilities, we facilitate UniPose with a mixture of visual encoders, among them a pose-specific visual encoder. Benefiting from a unified learning strategy, UniPose effectively transfers knowledge across different pose-relevant tasks, adapts to unseen tasks, and exhibits extended capabilities. This work serves as the first attempt at building a general-purpose framework for pose comprehension, generation, and editing. Extensive experiments highlight UniPose’s competitive and even superior performance across various pose-relevant tasks. Code is available at https://github.com/liyiheng23/UniPose.

In this paper, we present a new perspective on analyzing the unique visual patterns specific to each speaker in talking head videos. Traditional speaker recognition methods primarily rely on static facial appearance. In contrast, our method emphasizes dynamic visual patterns, which we denote as Dynamic Visual speaking Patterns (DVPs). By concentrating on dynamic patterns, our approach not only facilitates speaker recognition but also inherently resists fake samples, with the potential to accurately identify the original speaker even in manipulated videos. To guide the model in learning DVPs beyond superficial appearance, we introduce three seamlessly integrable improvements to existing speaker recognition works: (1) Input level: We introduce negative samples that retain the original static facial features but distort dynamic patterns intentionally. The model then learns to distinguish dynamic patterns across different speakers effectively With the strategy of contrastive learning. (2) Feature level: We convert the input video frames from the spatio-temporal domain to the frequencytemporal domain, facilitating an easy distinction between static and dynamic patterns. (3) Learning strategy: We incorporate a Gradient Reversal Layer (GRL) to mitigate the model’s reliance on static features, forcing it to focus on dynamic patterns. We finally validate our work comprehensively based on a simple speaker recognition framework. Experimental results show that our method not only excels in speaker recognition tasks but also inherently resists manipulation in forged video samples. Moreover, the dynamic visual patterns lead to a new challenge task in talking head video analysis: Identifying Original Speaker in forged videos (IOS). The advantages of our proposed DVPs over traditional static speaker representations highlight the potential of DVPs for robust speaker recognition in both real and manipulated videos.

The progress on generative models has led to significant advances on text-to-video (T2V) generation, yet the motion controllability of generated videos remains limited. Existing motion transfer methods explored the motion representations of reference videos to guide generation. Nevertheless, these methods typically rely on sample-specific optimization strategy, resulting in high computational burdens. In this paper, we propose EfficientMT, a novel and efficient end-to-end framework for video motion transfer. By leveraging a small set of synthetic paired motion transfer samples, EfficientMT effectively adapts a pretrained T2V model into a general motion transfer framework that can accurately capture and reproduce diverse motion patterns. Specifically, we repurpose the backbone of the T2V model to extract temporal information from reference videos, and further propose a scaler module to distill motion-related information. Subsequently, we introduce a temporal integration mechanism that seamlessly incorporates reference motion features into the video generation process. After training on our self-collected synthetic paired samples, EfficientMT enables general video motion transfer without requiring test-time optimization. Extensive experiments demonstrate that our EfficientMT outperforms existing methods in efficiency while maintaining flexible motion controllability. Our code will be available https://github.com/PrototypeNx/EfficientMT.

Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra network that learns to follow a condition image. However, for every single condition type, ControlNet requires independent training on millions of data pairs with hundreds of GPU hours, which is quite expensive and makes it challenging for ordinary users to explore and develop new types of conditions. To address this problem, we propose the CtrLoRA framework, which trains a Base ControlNet to learn the common knowledge of image-to-image generation from multiple base conditions, along with condition-specific LoRAs to capture distinct characteristics of each condition. Utilizing our pretrained Base ControlNet, users can easily adapt it to new conditions, requiring as few as 1,000 data pairs and less than one hour of single-GPU training to obtain satisfactory results in most scenarios. Moreover, our CtrLoRA reduces the learnable parameters by 90% compared to ControlNet, significantly lowering the threshold to distribute and deploy the model weights. Extensive experiments on various types of conditions demonstrate the efficiency and effectiveness of our method. Codes and model weights will be released at https://github.com/xyfJASON/ctrlora.

Currently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs). However, most benchmarks conduct questions by selecting images from existing datasets, resulting in the potential data leakage. Besides, these benchmarks merely focus on evaluating LVLMs on the realistic style images and clean scenarios, leaving the multi-stylized images and noisy scenarios unexplored. In response to these challenges, we propose a dynamic and scalable benchmark named Dysca for evaluating LVLMs by leveraging synthesis images. Specifically, we leverage Stable Diffusion and design a rule-based method to dynamically generate novel images, questions and the corresponding answers. We consider 51 kinds of image styles and evaluate the perception capability in 20 subtasks. Moreover, we conduct evaluations under 4 scenarios (i.e., Clean, Corruption, Print Attacking and Adversarial Attacking) and 3 question types (i.e., Multi-choices, True-or-false and Free-form). Thanks to the generative paradigm, Dysca serves as a scalable benchmark for easily adding new subtasks and scenarios. A total of 24 advanced open-source LVLMs and 2 close-source LVLMs are evaluated on Dysca, revealing the drawbacks of current LVLMs. The benchmark is released in anonymous github page \url{https://github.com/Benchmark-Dysca/Dysca}.

Large audio-language models (LALMs), built upon powerful Large Language Models (LLMs), have exhibited remarkable audio comprehension and reasoning capabilities. However, the training of LALMs demands a large corpus of audio-language pairs, which requires substantial costs in both data collection and training resources. In this paper, we propose MATS, an audio-language multimodal LLM designed to handle Multiple Audio task using solely Text-only Supervision. By leveraging pre-trained audio-language alignment models such as CLAP, we develop a text-only training strategy that projects the shared audio-language latent space into LLM latent space, endowing the LLM with audio comprehension capabilities without relying on audio data during training. To further bridge the modality gap between audio and language embeddings within CLAP, we propose the Strongly-related noisy text with audio (Santa) mechanism. Santa maps audio embeddings into CLAP language embedding space while preserving essential information from the audio input. Extensive experiments demonstrate that MATS, despite being trained exclusively on text data, achieves competitive performance compared to recent LALMs trained on large-scale audio-language pairs. The code is publicly available in https://github.com/wangwen-banban/MATS

Robots rely heavily on visual perception to understand and interact with complex environments. To support this capability, modern perception models have become increasingly large and powerful, resulting in high computational costs that hinder their real-time performance in robotic applications. Existing acceleration techniques, such as model pruning and token pruning, focus on reducing architectural or parameter redundancy but still process all object categories, regardless of task requirements. However, in real-world robotic scenarios, different tasks typically require only a subset of object categories. For instance, a service robot may focus on kitchenware while cooking, but shift to furniture and obstacles while cleaning. This task-dependent variation creates opportunities to reduce computational cost by selectively processing relevant information. Existing methods are not designed to exploit this potential for task-specific efficiency. To address this limitation, we propose TaskTP, a task-oriented token pruning method that dynamically adjusts token pruning based on the target category set. A dynamic gating network is introduced between successive Transformer blocks, which evaluates the relevance of each token to the given task. TaskTP allows for more aggressive pruning when fewer categories are required, optimizing computation without sacrificing performance. After a task-agnostic training phase, it can be flexibly configured at deployment time to support any category subset without retraining, making it both efficient and versatile. TaskTP improves the performance of Mask R-CNN from 31.4 fps to 38.5 fps on the COCO dataset. Furthermore, on the ScanNet dataset, where an object search task was defined to simulate real-world robotic applications, processing time was reduced from 3197 ms to 2437 ms, demonstrating significant efficiency gains.

Zero-shot generalization across various robots, tasks and environments remains a significant challenge in robotic manipulation. Policy code generation methods use executable code to connect high-level task descriptions and low-level action sequences, leveraging the generalization capabilities of large language models and atomic skill libraries. In this work, we propose Robotic Programmer (RoboPro), a robotic foundation model, enabling the capability of perceiving visual information and following free-form instructions to perform robotic manipulation with policy code in a zero-shot manner. To address low efficiency and high cost in collecting runtime code data for robotic tasks, we devise Video2Code to synthesize executable code from extensive videos in-the-wild with off-the-shelf vision-language model and code-domain large language model. Extensive experiments show that RoboPro achieves the state-of-the-art zero-shot performance on robotic manipulation in both simulators and real-world environments. Specifically, the zero-shot success rate of RoboPro on RLBench surpasses Code-as-Policies equipped with the state-of-the-art model GPT-4o by 11.6%. Furthermore, RoboPro is robust to variations on API formats and skill sets. Our website can be found at https://video2code.github.io/RoboPro-website/.

Contrastive Language-Image Pre-training (CLIP) has become a foundation model and has been applied to various vision and multimodal tasks. However, recent works indicate that CLIP falls short in distinguishing detailed differences in images and shows suboptimal performance on dense-prediction and vision-centric multimodal tasks. Therefore, this work focuses on improving existing CLIP models, aiming to capture as many visual details in images as possible. We find that a specific type of generative models, unCLIP, provides a suitable framework for achieving our goal. Specifically, unCLIP trains an image generator conditioned on the CLIP image embedding. In other words, it inverts the CLIP image encoder. Compared to discriminative models like CLIP, generative models are better at capturing image details because they are trained to learn the data distribution of images. Additionally, the conditional input space of unCLIP aligns with CLIP's original image-text embedding space. Therefore, we propose to invert unCLIP (dubbed un$^2$CLIP) to improve the CLIP model. In this way, the improved image encoder can gain unCLIP's visual detail capturing ability while preserving its alignment with the original text encoder simultaneously. We evaluate our improved CLIP across various tasks to which CLIP has been applied, including the challenging MMVP-VLM benchmark, the dense-prediction open-vocabulary segmentation task, and multimodal large language model tasks. Experiments show that un$^2$CLIP significantly improves the original CLIP and previous CLIP improvement methods. Code and models are available at https://github.com/LiYinqi/un2CLIP.

Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their efficiency and ease of deployment, existing approaches often underexploit the rich information embedded in the model’s logits space. In this paper, we propose LogitGap, a novel post-hoc OOD detection method that explicitly exploits the relationship between the maximum logit and the remaining logits to enhance the separability between in-distribution (ID) and OOD samples. To further improve its effectiveness, we refine LogitGap by focusing on a more compact and informative subset of the logit space. Specifically, we introduce a training-free strategy that automatically identifies the most informative logits for scoring. We provide both theoretical analysis and empirical evidence to validate the effectiveness of our approach. Extensive experiments on both vision-language and vision-only models demonstrate that LogitGap consistently achieves state-of-the-art performance across diverse OOD detection scenarios and benchmarks.

Designing protein sequences that fold into a target 3D structure—known as protein inverse folding—is a fundamental challenge in protein engineering. While recent deep learning methods have achieved impressive performance by recovering native sequences, they often overlook the one-to-many nature of the problem: multiple diverse sequences can fold into the same structure. This motivates the need for a generative model capable of designing diverse sequences while preserving structural consistency. To address this trade-off, we introduce ProtInvTree, the first reward-guided tree-search framework for protein inverse folding. ProtInvTree reformulates sequence generation as a deliberate, step-wise decision-making process, enabling the exploration of multiple design paths and exploitation of promising candidates through self-evaluation, lookahead, and backtracking. We propose a two-stage focus-and-grounding action mechanism that decouples position selection and residue generation. To efficiently evaluate intermediate states, we introduce a jumpy denoising strategy that avoids full rollouts. Built upon pretrained protein language models, ProtInvTree supports flexible test-time scaling by adjusting the search depth and breadth without retraining. Empirically, ProtInvTree outperforms state-of-the-art baselines across multiple benchmarks, generating structurally consistent yet diverse sequences, including those far from the native ground truth. The code is available at https://github.com/A4Bio/ProteinInvBench/.

The molecular large language models have garnered widespread attention due to their promising potential on molecular applications. However, current molecular large language models face significant limitations in understanding molecules due to inadequate textual descriptions and suboptimal molecular representation strategies during pretraining. To address these challenges, we introduce KnowMol-100K, a large-scale dataset with 100K fine-grained molecular annotations across multiple levels, bridging the gap between molecules and textual descriptions. Additionally, we propose chemically-informative molecular representation, effectively addressing limitations in existing molecular representation strategies. Building upon these innovations, we develop KnowMol, a state-of-the-art multi-modal molecular large language model. Extensive experiments demonstrate that KnowMol achieves superior performance across molecular understanding and generation tasks.

Visual recognition models pretrained on clean images usually do not perform well in the presence of image corruptions, such as blurring or noise, which limits their applicability in real-world scenarios. To solve this problem, existing approaches usually design complex data augmentations to train a robust model from scratch or adapt a pretrained model to corrupted scenarios. These approaches ignore the existence of the large number of deployed models in our community, causing extensive computation and storage costs for making deployed models adapted. Based on this consideration, this paper focuses on solving a practical problem of making many clean-image-pretrained models adapt to unlabeled corrupted images through one training procedure. To this end, we aim to learn a Plug-and-play Image Translator (PIT) that can be directly combined with recognition models after training. Existing approaches, such as vanilla image translation and restoration, are not proper for solving this problem, as they are mostly based on supervised training and are not recognition-oriented. To address this issue, we propose a recognition-oriented unsupervised image translation framework to make PIT produce images with indistinguishable recognition predictions from the clean ones. We verify the effectiveness of PIT on several recognition tasks and show that PIT boosts the performance of clean-image-pretrained models significantly in the presence of image corruptions.

Black-Box Knowledge Distillation (B2KD) is a conservative task in cloud-to-edge model compression, emphasizing the protection of data privacy and model copyrights on both the cloud and edge. With invisible data and models hosted on the server, B2KD aims to utilize only the API queries of the teacher model's inference results in the cloud to effectively distill a lightweight student model deployed on edge devices. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity in data distribution. To address these issues, we theoretically provide a new optimization direction from logits to cell boundary, different from direct logits alignment, and formalize a workflow comprising deprivatization, distillation, and adaptation at test time. Guided by this, we propose a method, Mapping-Emulation KD (MEKD), to enhance the robust prediction and anti-interference capabilities of the student model on edge devices for any unknown data distribution in real-world scenarios. Our method does not differentiate between treating soft or hard responses and consists of: 1) deprivatization: emulating the inverse mapping of the teacher function with a generator, 2) distillation: aligning low-dimensional logits of the teacher and student models by reducing the distance of high-dimensional image points, and 3) adaptation: correcting the student's online prediction bias through a graph propagation-based only-forward test-time adaptation algorithm. Our method demonstrates inspiring performance for edge model distillation and adaptation across different teacher-student pairs. We validate the effectiveness of our method on multiple image recognition benchmarks and various Deep Neural Network models, achieving state-of-the-art performance and showcasing its practical value in remote sensing image recognition applications.

Out-of-distribution (OOD) detection presents a significant challenge in deploying pattern recognition and machine learning models, as they frequently fail to generalize to data from unseen distributions. Recent advancements in vision-language models (VLMs), particularly CLIP, have demonstrated promising results in OOD detection through their rich multimodal representations. However, current CLIP-based OOD detection methods predominantly rely on single-modality in-distribution (ID) data (e.g., textual cues), overlooking the valuable information contained in ID visual cues. In this work, we demonstrate that incorporating ID visual information is crucial for unlocking CLIP's full potential in OOD detection. We propose a novel approach, Dual-Pattern Matching (DPM), which effectively adapts CLIP for OOD detection by jointly exploiting both textual and visual ID patterns. Specifically, DPM refines visual and textual features through the proposed Domain-Specific Feature Aggregation (DSFA) and Prompt Enhancement (PE) modules. Subsequently, DPM stores class-wise textual features as textual patterns and aggregates ID visual features as visual patterns. During inference, DPM calculates similarity scores relative to both patterns to identify OOD data. Furthermore, we enhance DPM with lightweight adaptation mechanisms to further boost OOD detection performance. Comprehensive experiments demonstrate that DPM surpasses state-of-the-art methods on multiple benchmarks, highlighting the effectiveness of leveraging multimodal information for OOD detection. The proposed dual-pattern approach provides a simple yet robust framework for leveraging vision-language representations in OOD detection tasks.

Facial recognition (FR) technology offers convenience in our daily lives, but it also raises serious privacy issues due to unauthorized FR applications. To protect facial privacy, existing methods have proposed adversarial face examples that can fool FR systems. However, most of these methods work only in the digital domain and do not consider natural physical protections. In this paper, we present NatMask, a 3D-based method for creating natural and realistic adversarial face masks that can preserve facial identity in the physical world. Our method utilizes 3D face reconstruction and differentiable rendering to generate 2D face images with natural-looking facial masks. Moreover, we propose an identity-aware style injection (IASI) method to improve the naturalness and transferability of the mask texture. We evaluate our method on two face datasets to verify its effectiveness in protecting face identity against four state-of-the-art (SOTA) FR models and three commercial FR APIs in both digital and physical domains under black-box impersonation and dodging strategies. Experiments show that our method can generate adversarial masks with superior naturalness and physical realizability to safeguard face identity, outperforming SOTA methods by a large margin.

Contrastive Language-Image Pre-training (CLIP) models exhibit impressive zero-shot performance across various downstream cross-modal tasks by simply computing the dot product between image and text features. CLIP is pre-trained on large-scale image-text pairs using the InfoNCE loss, which maximizes the cosine similarity of positive image-text pairs while minimizing the similarity of negative pairs. However, an objective mismatch exists between the downstream usage and the pre-training phase, as the inference phase fails to exploit information from negative samples. Intuitively, since the CLIP model has been optimized based on the InfoNCE loss, the downstream usage should also be in alignment. In this paper, we start from analyzing the InfoNCE loss and derive its upper bound. Our derivation reveals that the dot-product operation serves a zero-order approximation of this upper bound, while a centralization operation represents a first-order approximation. To address the objective mismatch problem, we propose a novel method, Inference Calibration (IC), which leverages the first-order and second-order moments of data distribution to calibrate features for zero-shot and few-shot scenarios. Experiments on various cross-modal tasks demonstrate the effectiveness of IC in both zero-shot and few-shot scenarios over dot-product operation and other comparative methods. • The downstream usage of CLIP model mismatches its pre-training objective. • Previous popular inference methods are the approximation of pre-training objective. • Proposed method mitigate objective mismatch problem under both zero-shot and few-shot settings.

Atypical facial expressions during interaction are among the early symptoms of autism spectrum disorder (ASD) and are included in standard diagnostic assessments. However, current methods rely on subjective human judgments, introducing bias and limiting objectivity. This paper proposes an automated framework for objective and quantitative assessment of autistic children's facial expressions during social play. Initially, we utilize four synchronized cameras to record interactions between ASD children and teachers during structured activities dominated by the teacher. To address challenges posed by head movements and occluded faces, we introduce a multi-view facial expression recognition strategy. Its effectiveness is demonstrated by experiments in real-world applications. To quantify the patterns of affect status and the dynamic complexity of facial expressions, we use the temporally accumulated distribution of the basic facial expressions and the multi-dimensional multiscale entropy of the facial expression sequence. Analysis of these features revealed significant differences between ASD and TD groups. Experimental results, derived from our quantified features, confirm conclusions drawn from previous research and experiential observations. With these facial expression features, ASD and typically developing (TD) children are accurately classified (accuracy 92.1%, precision 94.4%, 89.5% sensitivity, 94.7% specificity) in empirical experiments, suggesting the potential of our framework for improved ASD assessment.

Visually impaired individuals encounter significant challenges when walking and acting in unfamiliar environments, particularly in outdoor scenarios. The complexity of outdoor environments, characterized by diverse obstacles, traffic signals, and societal norms, poses substantial barriers to mobility of visually impaired individuals and makes long-distance walking especially arduous. Although GPS-based navigation systems can facilitate long-distance travel, they often suffer from location inaccuracies in urban areas and even completely fail indoors. Moreover, these systems lack the capability to provide detailed information about walkways and immediate surroundings, which are crucial for safe and efficient walking. To address these limitations, we introduce a proof-of-concept wearable navigation system named eLabrador, designed to assist visually impaired individuals in long-distance walking in unfamiliar outdoor environments. The eLabrador integrates public maps (e.g. Amap or Google Maps) and GPS for global route planning, while leveraging computational visual perception to provide precise and safe local guidance. This hybrid approach enables accurate and safe navigation for visually impaired individuals in outdoor scenarios. Specifically, the eLabrador utilizes a head-mounted RGB-D camera to capture environmental geometric terrain and objects in outdoor urban environments. These inputs are processed into a 3D semantic map, offering a detailed representation of the surrounding environment. The planning module then integrates this 3D semantic map with route information from the global map (i.e. Amap) to generate an optimized walking path. Finally, the interaction module utilizes the audio-haptic dual-channel to relay navigation instructions to visually impaired user. Together, these three modules work seamlessly to facilitate long-distance navigation for visually impaired individuals in outdoor environments. The eLabrador is evaluated with two real-world outdoor scenarios, involving 10 visually impaired and visually masked participants. The experiments show that eLabrador successfully guides visually impaired participants to their destinations in outdoor environments. Additionally, the eLabrador provides descriptive information about landmarks and other navigation cues, helping visually impaired users better understand their surroundings. Subjective evaluations further indicate that most participants felt a sense of safety and reported an acceptable cognitive load during navigation, indicating its usability and effectiveness. Note to Practitioners—Visually impaired individuals almost cannot walk long distance in unfamiliar outdoor environments. Without proper assistance, their mobility and quality of life can be severely impacted. To address this issue, this article presents a wearable navigation system eLabrador to assist visually impaired individuals in walking outdoors, such as traveling from a residential entrance to a nearby park. Experimental results from real-world scenarios involving 10 participants demonstrate that eLabrador safely guides visually impaired users to their destination, significantly enhancing their mobility and independence.

In recent years, the Vision Transformer (ViT) model has gradually become mainstream in various computer vision tasks, and the robustness of the model has received increasing attention. However, existing large models tend to prioritize performance during training, potentially neglecting the robustness, which may lead to serious security concerns. In this paper, we establish a new challenge: exploring how to use a small number of additional parameters for adversarial finetuning to quickly and effectively enhance the adversarial robustness of a standardly trained model. To address this challenge, we develop novel LNLoRA module, incorporating a learnable layer normalization before the conventional LoRA module, which helps mitigate magnitude differences in parameters between the adversarial and standard training paradigms. Furthermore, we propose the FullLoRA framework by integrating the learnable LNLoRA modules into all key components of ViT-based models while keeping the pretrained model frozen, which can significantly improve the model robustness via adversarial finetuning in a parameter-efficient manner. Extensive experiments on several datasets demonstrate the superiority of our proposed FullLoRA framework. It achieves comparable robustness with full finetuning while only requiring about 5% of the learnable parameters. This also effectively addresses concerns regarding extra model storage space and enormous training time caused by adversarial finetuning.

Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features. However, we observe that relying solely on clothes-irrelevant features for clothes-changing re-id is limited, since they often lack adequate identity information and suffer from large intra-class variations. On the contrary, clothes-relevant features can be used to discover same-clothes intermediaries that possess informative identity clues. Based on this observation, we propose a Feasibility-Aware Intermediary Matching (FAIM) framework to additionally utilizeclothes-relevant featuresfor retrieval. First, an Intermediary Matching (IM) module is designed to perform an intermediary-assisted matching process. This process involves using clothes-relevant features to find informative intermediates, and then using clothes-irrelevant features of these intermediates to complete the matching. Second, in order to reduce the negative effect of low-quality intermediaries, an Intermediary-Based Feasibility Weighting (IBFW) module is designed to evaluate the feasibility of intermediary matching process by assessing the quality of intermediaries. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on several widely-used clothes-changing re-id benchmarks.

Recognition in open-world scenarios is an important and challenging field, where Vision-Language Pre-training paradigms have greatly impacted the 2D domain. This inspires a growing interest in introducing 2D pre-trained models, such as CLIP, into the 3D domain to enhance the ability of point cloud understanding. Considering the difference between discrete 3D point clouds and real-world 2D images, reducing the domain gap is crucial. Some recent works project point clouds onto a 2D plane to enable 3D zero-shot capabilities without training. However, this simplistic approach leads to an unclear or even distorted geometric structure, limiting the potential of 2D pre-trained models in 3D. To address the domain gap, we propose Point2Real, a training-free framework based on the realistic rendering technique to automate the transformation of the 3D point cloud domain into the Vision-Language domain. Specifically, Point2Real leverages a shape recovery module that devises an iterative ball-pivoting algorithm to convert point clouds into meshes, narrowing the gap in shape at first. To simulate photo-realistic images, a set of refined textures as candidates is applied for rendering, where the CLIP confidence is utilized to select the suitable one. Moreover, to tackle the viewpoint challenge, a heuristic multi-view adapter is implemented for feature aggregation, which exploits the depth surface as an effective indicator of view-specific discriminability for recognition. We conduct experiments on ModelNet10, ModelNet40, and ScanObjectNN datasets, and the results demonstrate that Point2Real outperforms other approaches in zero-shot and few-shot tasks by a large margin.

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps, which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames, their forecasting should have intrinsic correlation, such as overlapping predicted trajectories should be consistent, or be different but share the same motion goal depending on the road situation. Motivated by this, in this work, we introduce HPNet, a novel dynamic trajectory forecasting method. Aiming for stable and accurate trajectory forecasting, our method leverages not only historical frames including maps and agent states, but also historical predictions. Specifically, we newly design a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Besides, it also extends the attention range beyond the currently visible window benefitting from the use of historical predictions. The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module, serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HP-Net achieves state-of-the-art performance, and generates accurate and stable future trajectories. Our code are available at https://github.com/XiaolongTang23/HPNet.

We propose a novel strategy, ES3, for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video, we reframe the problem as the acquisition of shared, unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation, we propose a novel “evolving” strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First, we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next, we incorporate video-unique speech information and bootstrap the audio-visual repre-sentations on top of the previously acquired shared knowledge. Finally, we maximize the total audio-visual speech information, including synergistic information to obtain robust and comprehensive representations. We implement ES3as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular, on LRS2-BBC, our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

It was our pleasure and privilege to welcome you to Istanbul for the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024). We hope your experience at FG was rewarding both professionally and personally!

3D GAN inversion enables not only 3D reconstruction from a 2D image, but also novel view synthesis and image editing. Existing works ensure the novel view synthesis quality by constraining the synthesized views to conform to the real image distribution. However, most of the methods did not consider the multi-view consistency, i.e., different photos of the same 3D scene via 3D GAN inversion can be inverted to the same 3D scene. In this paper, we propose a bidirectional encoder (BiDiE) for 3D GAN inversion that can improve the multi-view consistency and alleviate the interference of camera parameter prediction errors. On the one hand, the bidirectional encoder takes real images as input, estimates the camera parameters, and performs 3D reconstruction. On the other hand, the bidirectional encoder takes randomly sampled latent code and camera parameters as input, and generates synthesized images to assist in the latent code learning process. In addition, we extend the latent space from W+ to W++ to improve its reconstruction and editing capabilities. Experiments on the FFHQ, CelebA-HQ and Multi-PIE datasets prove that our proposed method outperforms state-of-the-art methods in multi-view consistent reconstruction as well as editing capability.11Code and datasets are available at https://github.com/WHZMM/BiDiE

In this paper, we propose a novel modular network framework, called Scalable Modular Network (SMN), which enables adaptive learning capability and supports integration of new modules after pre-training for better adaptation. This adaptive capability comes from a novel design of router within SMN, named agreement router, which selects and composes different specialist modules through an iterative message passing process. The agreement router iteratively computes the agreements among a set of input and outputs of all modules to allocate inputs to specific module. During the iterative routing, messages of modules are passed to each other, which improves the module selection process with consideration of both local interactions (between a single module and input) and global interactions involving multiple other modules. To validate our contributions, we conduct experiments on two problems: a toy min-max game and few-shot image classification task. Our experimental results demonstrate that SMN can generalize to new distributions and exhibit sample-efficient adaptation to new tasks. Furthermore, SMN can achieve a better adaptation capability when new modules are introduced after pre-training. Our code is available at https://github.com/hu-my/ScalableModularNetwork.

Vision Transformers (ViTs) are now flourishing in the computer vision area. Despite the remarkable success, ViTs suffer from high computational costs, which greatly hinder their practical usage. Token reduction, which identifies and discards unimportant tokens during forward propagation, has then been proposed to make ViTs more efficient. For token reduction methodologies, a scoring metric is essential to distinguish between important and unimportant tokens. The attention score from the $\mathrm{[CLS]}$ token, which takes the responsibility to aggregate useful information and form the final output, has been established by prior works as an advantageous choice. Nevertheless, whereas the task pressure is applied at the end of the whole model, token reduction generally starts from very early blocks. Given the long distance in between, in the early blocks, $\mathrm{[CLS]}$ token lacks the impetus to gather task-relevant information, causing somewhat arbitrary attention allocation. This phenomenon, in turn, degrades the reliability of token scoring and substantially compromises the effectiveness of token reduction. Inspired by advances in the domain of dynamic neural networks, in this paper, we introduce Multi-Exit Token Reduction (METR), a simple romance between multi-exit architecture and token reduction—two areas previously considered orthogonal. By injecting early task pressure via multi-exit loss, the $\mathrm{[CLS]}$ token is spurred to collect task-related information in even early blocks, thus bolstering the credibility of $\mathrm{[CLS]}$ attention as a token-scoring metric. Additionally, we employ self-distillation to further refine the quality of early supervision. Extensive experiments substantiate both the existence and effectiveness of the newfound chemistry. Comparative assessments also indicate that METR outperforms state-of-the-art token reduction methods on standard benchmarks, especially under aggressive reduction ratios.

Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could be costly. In this work, we aim to leverage unlabeled data that naturally spans multiple domains to enhance the transferability of vision-language models. Under this unsupervised multi-domain setting, we have identified inherent model bias within CLIP, notably in its visual and text encoders. Specifically, we observe that CLIP’s visual encoder tends to prioritize encoding domain over discriminative category information, meanwhile its text encoder exhibits a preference for domain-relevant classes. To mitigate this model bias, we propose a training-free and label-free feature calibration method, Unsupervised Multi-domain Feature Calibration (UMFC). UMFC estimates image-level biases from domain-specific features and text-level biases from the direction of domain transition. These biases are subsequently subtracted from original image and text features separately, to render them domain-invariant. We evaluate our method on multiple settings including transductive learning and test-time adaptation. Extensive experiments show that our method outperforms CLIP and performs on par with the state-of-the-arts that need additional annotations or optimization. Our code is available at https://github.com/GIT-LJc/UMFC.

Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. However, some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. In this paper, we construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue, in which we divide the test samples into subsets with different semantic and covariate shift degrees relative to the ID dataset. The data division is achieved through a shift measuring method based on our proposed Language Aligned Image feature Decomposition (LAID). Moreover, we construct a Synthetic Incremental Shift (Syn-IS) dataset that contains high-quality generated images with more diverse covariate contents to complement the IS-OOD benchmark. We evaluate current OOD detection methods on our benchmark and find several important insights: (1) The performance of most OOD detection methods significantly improves as the semantic shift increases; (2) Some methods like GradNorm may have different OOD detection mechanisms as they rely less on semantic shifts to make decisions; (3) Excessive covariate shifts in the image are also likely to be considered as OOD for some methods. Our code and data are released in https://github.com/qqwsad5/IS-OOD.

People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a recognition model with such principles to explain the recognition process to enhance human trust. We propose Semantic Prototype Analysis Network (SPANet), an interpretable object recognition approach that enables models to explicate the decision process more lucidly and comprehensibly to humans by "pointing out where to focus" and "telling about why it is" simultaneously. With the proposed method, some part prototypes with semantic concepts will be provided to elaborate on the classification together with a group of visualized samples to achieve both part-wise and semantic interpretability. The results of extensive experiments demonstrate that SPANet is able to recognize objects almost as well as the non-interpretable models, at the same time generating intelligible explanations for its decision process.

Unsupervised domain adaptation (UDA), which aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, is useful for various cross-domain image classification scenarios. A commonly used approach for UDA is to minimize the distribution differences between two domains, and subdomain alignment is found to be an effective method. However, most of the existing subdomain alignment methods are based on adversarial learning and focus on subdomain alignment procedures without considering the discriminability among individual subdomains, resulting in slow convergence and unsatisfactory adaptation results. To address these issues, we propose a novel deep subdomain alignment method for UDA in image classification, which consists of a Union Subdo-main Contrastive Learning (USCL) module and a Multi-view Subdomain Alignment (MvSA) strategy. USCL can create discriminative and dispersed subdomains by bringing samples from the same subdomain closer while pushing away samples from different subdomains. MvSA makes use of labeled source domain data and easy target domain data to perform target-to-source and target-to-target alignment. Experimental results on three image classifi-cation datasets (Office-31, Office-Home, Visda-17) demonstrate that our proposed method is effective for UDA and achieves promising results in several cross-domain image classification tasks. Our code will be available: https://github.com/zhaoyewei/DSACDIC.

Semi-supervised learning (SSL) suffers from severe performance degradation when labeled and unlabeled data come from inconsistent and imbalanced distribution. Nonetheless, there is a lack of theoretical guidance regarding a remedy for this issue. To bridge the gap between theoretical insights and practical solutions, we embark to an analysis of generalization bound of classic SSL algorithms. This analysis reveals that distribution inconsistency between unlabeled and labeled data can cause a significant generalization error bound. Motivated by this theoretical insight, we present a Triplet Adaptation Framework (TAF) to reduce the distribution divergence and improve the generalization of SSL models. TAF comprises three adapters: Balanced Residual Adapter, aiming to map the class distribution of labeled and unlabeled data to a uniform distribution for reducing class distribution divergence; Representation Adapter, aiming to map the representation distribution of unlabeled data to labeled one for reducing representation distribution divergence; and Pseudo-Label Adapter, aiming to align the predicted pseudo-labels with the class distribution of unlabeled data, thereby preventing erroneous pseudo-labels from exacerbating representation divergence. These three adapters collaborate synergistically to reduce the generalization bound, ultimately achieving a more robust and generalizable SSL model. Extensive experiments across various robust SSL scenarios validate the efficacy of our method.

Deepfake represented by face swapping and face reenactment can transfer the appearance and behavioral expressions of a face in one video image to another face in a different video. In recent years, with the advancement of deep learning techniques, deepfake technology has developed rapidly, achieving increasingly realistic effects. Therefore, many researchers have begun to study deepfake detection research. However, most existing studies on deepfake detection are mainly limited to binary classification of real and fake images, rather than identifying different methods in an open-world scenario, leading to failures in dealing with unknown deepfake categories in practice. In this paper, we propose an unsupervised domain adaptation method for fine-grained open-set deepfake detection. Our method first uses labeled data from the source domain for model pre-training to establish the ability of recognizing different deepfake methods in the source domain. Then, the method uses a Network Memorization based Adaptive Clustering (NMAC) approach to cluster unlabeled images in the target domain and designs a Pseudo-Label Generation (PLG) to generate virtual class labels for unknown deepfake categories by matching the adaptive clustering results with the known deepfake categories in the source domain. Finally, we retrain the initial multi-class deepfake detection model using labeled data of the source domain and pseudo-labeled data of the target domain to improve its generalization ability to unknown deepfake classes presented in the target domain. We validate the effectiveness of the proposed method under multiple open-set fine-grained deepfake detection tasks based on three deepfake datasets (ForgerNet, FaceForensics++, and FakeAVCeleb). Experimental results show that our method has better domain generalization ability than the state-of-the-art methods, and achieves promising performance in fine-grained open-set deepfake detection.

Video question answering (VideoQA) is challenging since it requires the model to extract and combine multi-level visual concepts from local objects to global actions from complex events for compositional reasoning. Existing works represent the video with fixed-duration clip features that make the model struggle in capturing the crucial concepts in multiple granularities. To overcome this shortcoming, we propose to represent the video with an Event Graph in a hierarchical structure whose nodes correspond to visual concepts of different levels (object, relation, scene and action) and edges indicate their spatial-temporal relationships. We further propose a H ierarchical S patial- T emporal T ransformer (HSTT) which takes nodes from the graph as visual input to realize compositional reasoning guided by the event graph. To fully exploit the spatial-temporal context delivered from the graph structure, on the one hand, we encode the nodes in the order of their semantic hierarchy (depth) and occurrence time (breadth) with our improved graph search algorithm; On the other hand, we introduce edge-guided attention to combine the spatial-temporal context among nodes according to their edge connections. HSTT then performs QA by cross-modal interactions guaranteed by the hierarchical correspondence between the multi-level event graph and the cross-level question. Experiments on the recent challenging AGQA and STAR datasets show that the proposed method clearly outperforms the existing VideoQA models by a large margin, including those pre-trained with large-scale external data. Our code is available at https://github.com/ByZ0e/HSTT.

Attributed to the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of large poses and uncontrolled occlusions. In this paper, we propose a novel bypass enhanced representation learning (BERL) method to improve face recognition under unconstrained scenarios. The proposed method integrates self-supervised learning and supervised learning together by attaching two auxiliary bypasses, a 3D reconstruction bypass and a blind inpainting bypass, to assist robust feature learning for face recognition. Among them, the 3D reconstruction bypass enforces the face recognition network to encode pose independent 3D facial information, which enhances the robustness to various poses. The blind inpainting bypass enforces the face recognition network to capture more facial context information for face inpainting, which enhances the robustness to occlusions. The whole framework is trained in end-to-end manner with two self-supervised tasks above and the classic supervised face identification task. During inference, the two auxiliary bypasses can be detached from the face recognition network, avoiding any additional computational overhead. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art methods. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.

Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.

Gaze estimation across domains has been explored recently because the training data are usually collected under controlled conditions while the trained gaze estimators are used in nature and diverse environments. However, due to privacy and efficiency concerns, simultaneous access to annotated source data and to-be-predicted target data can be challenging. In light of this, we present an unsupervised source-free domain adaptation approach for gaze estimation, which adapts a source-trained gaze estimator to unlabeled target domains without source data. We propose the Uncertainty Reduction Gaze Adaptation (UnReGA) framework, which achieves adaptation by reducing both sample and model uncertainty. Sample uncertainty is mitigated by enhancing image quality and making them gaze-estimation-friendly, whereas model uncertainty is reduced by minimizing prediction variance on the same inputs. Extensive experiments are conducted on six cross-domain tasks, demonstrating the effectiveness of UnReGA and its components. Results show that UnReGA outperforms other state-of-the-art cross-domain gaze estimation methods under both protocols, with and without source data. The code is available at https://github.com/caixin1998/UnReGA.

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two- view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. Code is available at https://github.com/JackYFL/DISC.

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

Domain generalization (DG) attempts to learn a model on source domains that can well generalize to unseen but different domains. The multiple source domains are innately different in distribution but intrinsically related to each other, e.g., from the same label space. To achieve a generalizable feature, most existing methods attempt to reduce the domain discrepancy by either learning domain-invariant feature, or additionally mining domain-specific feature. In the space of these features, the multiple source domains are either tightly aligned or not aligned at all, which both cannot fully take the advantage of complementary information from multiple domains. In order to preserve more complementary information from multiple domains at the meantime of reducing their domain gap, we propose that the multiple domains should not be tightly aligned but composite together, where all domains are pulled closer but still preserve their individuality respectively. This is achieved by using instance-adaptive classifier specified for each instance’s classification, where the instance-adaptive classifier is slightly deviated from a universal classifier shared by samples from all domains. This adaptive classifier deviation allows all instances from the same category but different domains to be dispersed around the class center rather than squeezed tightly, leading to better generalization for unseen domain samples. In result, the multiple domains are harmoniously composite centered on a universal core, like a dandelion, so this work is referred to as DandelionNet. Experiments on multiple DG benchmarks demonstrate that the proposed method can learn a model with better generalization and experiments on source free domain adaption also indicate the versatility.

The co-occurrence signals (e.g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR). Compared to RGB data, skeleton data provide a more efficient and concise option, and lay a good foundation for the co-occurrence exploration in CSLR. However, skeleton data are often used as a tool to assist visual grounding and have not attracted sufficient attention. In this paper, we propose a simple yet effective GCN-based approach, named CoSign, to incorporate Co-occurrence Signals and explore the potential of skeleton data in CSLR. Specifically, we propose a group-specific GCN to better exploit the knowledge of each signal and a complementary regularization to prevent complex co-adaptation across signals. Furthermore, we propose a two-stream framework that gradually fuses both static and dynamic information in skeleton data. Experimental results on three public CSLR datasets (PHOENIX14, PHOENIX14-T and CSL-Daily) show that the proposed CoSign achieves competitive performance with recent video-based approaches while reducing the computation cost during training.

Video Question Answering (VideoQA) has emerged as a vital tool to evaluate agents’ ability to understand human daily behaviors. Despite the recent success of large vision language models in many multi-modal tasks, complex situation reasoning over videos involving multiple human-object interaction events still remains challenging. In contrast, humans can easily tackle it by using a series of episode memories as anchors to quickly locate question-related key moments for reasoning. To mimic this effective reasoning strategy, we propose the Glance- Focus model. One simple way is to apply an action detection model to predict a set of actions as key memories. However, these actions within a closed set vocabulary are hard to generalize to various video domains. Instead of that, we train an Encoder-Decoder to generate a set of dynamic event memories at the glancing stage. Apart from using supervised bipartite matching to obtain the event memories, we further design an unsupervised memory generation method to get rid of dependence on event annotations. Next, at the focusing stage, these event memories act as a bridge to establish the correlation between the questions with high-level event concepts and low-level lengthy video content. Given the question, the model first focuses on the generated key event memory, then focuses on the most relevant moment for reasoning through our designed multi-level cross- attention mechanism. We conduct extensive experiments on four Multi-Event VideoQA benchmarks including STAR, EgoTaskQA, AGQA, and NExT-QA. Our proposed model achieves state-of-the-art results, surpassing current large models in various challenging reasoning tasks. The code and models are available at https://github.com/ByZ0e/Glance-Focus.

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL by exploring two key questions: (1) How to quantify the relationship between \emph{ training} and \emph{novel} tasks? (2) How does the relationship affect the \emph{adaptation difficulty} on novel tasks for different models? To answer the first question, we propose Task Attribute Distance (TAD) as a metric to quantify the task relatedness via attributes. Unlike other metrics, TAD is independent of models, making it applicable to different FSL models. To address the second question, we utilize TAD metric to establish a theoretical connection between task relatedness and task adaptation difficulty. By deriving the generalization error bound on a novel task, we discover how TAD measures the adaptation difficulty on novel tasks for different models. To validate our theoretical results, we conduct experiments on three benchmarks. Our experimental results confirm that TAD metric effectively quantifies the task relatedness and reflects the adaptation difficulty on novel tasks for various FSL methods, even if some of them do not learn attributes explicitly or human-annotated attributes are not provided. Our code is available at \href{https://github.com/hu-my/TaskAttributeDistance}{https://github.com/hu-my/TaskAttributeDistance}.

Traditional semi-supervised learning (SSL) assumes that the feature distributions of labeled and unlabeled data are consistent which rarely holds in realistic scenarios. In this paper, we propose a novel SSL setting, where unlabeled samples are drawn from a mixed distribution that deviates from the feature distribution of labeled samples. Under this setting, previous SSL methods tend to predict wrong pseudo-labels with the model fitted on labeled data, resulting in noise accumulation. To tackle this issue, we propose \emph{Self-Supervised Feature Adaptation} (SSFA), a generic framework for improving SSL performance when labeled and unlabeled data come from different distributions. SSFA decouples the prediction of pseudo-labels from the current model to improve the quality of pseudo-labels. Particularly, SSFA incorporates a self-supervised task into the SSL framework and uses it to adapt the feature extractor of the model to the unlabeled data. In this way, the extracted features better fit the distribution of unlabeled data, thereby generating high-quality pseudo-labels. Extensive experiments show that our proposed SSFA is applicable to various pseudo-label-based SSL learners and significantly improves performance in labeled, unlabeled, and even unseen distributions.

The ability of few-shot learning (FSL) is a basic requirement of intelligent agent learning in the open visual world. However, existing deep learning systems rely too heavily on large numbers of training samples, making it hard to learn new categories efficiently from limited size of training data. Two key challenges of FSL are insufficient comprehension and imperfect modeling of the few-shot novel class. For insufficient visual comprehension, semantic knowledge which is information from other modalities can help replenish the understanding of novel classes. But even so, most works still suffer from the second challenge because the single global class prototype they adopted is extremely unstable and imperfect given the larger intra-class variation and harder inter-class discrimination in FSL scenario. Thus, we propose to represent each class by its several different parts with the help of class semantic knowledge. Since we can never pre-define parts for unknown novel classes, we embed them in a latent manner. Concretely, we train a generator that takes the class semantic knowledge as input and outputs several filters of class-specific semantic latent parts. By applying each part filter, our model can pay attention to corresponding local regions containing each part. At the inference stage, the classification is conducted by comparing the similarities between those parts. Experiments on several FSL benchmarks demonstrate the effectiveness of our proposed method and show its potential to go beyond class recognition to class understanding. Furthermore, we also find when semantic knowledge is more visualized and customized, it will be more helpful in the FSL task.

Alternatively inferring on the visual facts and commonsense is fundamental for an advanced visual question answering (VQA) system. This ability requires models to go beyond the literal understanding of commonsense. The system should not just treat objects as the entrance to query background knowledge, but fully ground commonsense to the visual world and imagine the possible relationships between objects, e.g., "fork, can lift, food". To comprehensively evaluate such abilities, we propose a VQA benchmark, Compositional Reasoning on vIsion and Commonsense(CRIC), which introduces new types of questions about CRIC, and an evaluation metric integrating the correctness of answering and commonsense grounding. To collect such questions and rich additional annotations to support the metric, we also propose an automatic algorithm to generate question samples from the scene graph associated with the images and the relevant knowledge graph. We further analyze several representative types of VQA models on the CRIC dataset. Experimental results show that grounding the commonsense to the image region and joint reasoning on vision and commonsense are still challenging for current approaches. The dataset is available at https://cricvqa.github.io.

Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution, which is prone to overfitting on tail classes and underfitting on head classes. In this work, we propose Dual Compensation Residual Networks to better fit both tail and head classes. First, we propose dual Feature Compensation Module (FCM) and Logit Compensation Module (LCM) to alleviate the overfitting issue. The design of these two modules is based on the observation: an important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. In details, the test features of a tail category tend to drift towards feature cloud of multiple similar head categories. So FCM estimates a multi-mode feature drift direction for each tail category and compensate for it. Furthermore, LCM translates the deterministic feature drift vector estimated by FCM along intra-class variations, so as to cover a larger effective compensation space, thereby better fitting the test features. Second, we propose a Residual Balanced Multi-Proxies Classifier (RBMC) to alleviate the under-fitting issue. Motivated by the observation that re-balancing strategy hinders the classifier from learning sufficient head knowledge and eventually causes underfitting, RBMC utilizes uniform learning with a residual path to facilitate classifier learning. Comprehensive experiments on Long-tailed and Class-Incremental benchmarks validate the efficacy of our method.

An object can be described as the combination of primary visual attributes. Disentangling such underlying primitives is the long-term objective of representation learning . It is observed that categories have natural hierarchical characteristics, i.e., any two objects can share some common primitives at a particular category level while possess unique traits at another. However, previous works usually operate in a flat manner (i.e., at a particular level) to disentangle the representations of objects. Even though they may obtain the primitives to constitute objects as the categories at that level, their results are obviously not efficient and complete. In this paper, we propose a Hierarchical Disentangling Network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner (i.e., level-wise), such that each level only focuses on learning the specific representations and finally the common and unique representations at all levels jointly constitute the raw object. Specifically, HDN is designed based on an encoder-decoder architecture. To simultaneously ensure the level-wise disentanglement and interpretability of the encoded representations, a novel hierarchical Generative Adversarial Network (GAN) is introduced. Quantitative and qualitative evaluations on popular object datasets validate the effectiveness of our method.

Scribble-supervised semantic segmentation is an appealing weakly supervised technique with low labeling cost. Existing approaches mainly consider diffusing the labeled region of scribble by low-level feature similarity to narrow the supervision gap between scribble labels and mask labels. In this study, we observe an annotation bias between scribble and object mask, i.e., label workers tend to scribble on the spacious region instead of corners. This label preference makes the model learn well on those frequently labeled regions but poor on rarely labeled pixels. Therefore, we propose BLPSeg to balance the label preference for complete segmentation. Specifically, the BLPSeg first predicts an annotation probability map to evaluate the rarity of labels on each image, then utilizes a novel BLP loss to balance the model training by up-weighting those rare annotations. Additionally, to further alleviate the impact of label preference, we design a local aggregation module (LAM) to propagate supervision from labeled to unlabeled regions in gradient backpropagation. We conduct extensive experiments to illustrate the effectiveness of our BLPSeg. Our single-stage method even outperforms other advanced multi-stage methods and achieves state-of-the-art performance.

Cross-modality face image synthesis such as sketch-to-photo, NIR-to-RGB, and RGB-to-depth has wide applications in face recognition, face animation, and digital entertainment. Conventional cross-modality synthesis methods usually require paired training data, i.e., each subject has images of both modalities. However, paired data can be difficult to acquire, while unpaired data commonly exist. In this paper, we propose a novel semi-supervised cross-modality synthesis method (namely CMOS-GAN), which can leverage both paired and unpaired face images to learn a robust cross-modality synthesis model. Specifically, CMOS-GAN uses a generator of encoder-decoder architecture for new modality synthesis. We leverage pixel-wise loss, adversarial loss, classification loss, and face feature loss to exploit the information from both paired multi-modality face images and unpaired face images for model learning. In addition, since we expect the synthetic new modality can also be helpful for improving face recognition accuracy, we further use a modified triplet loss to retain the discriminative features of the subject in the synthetic modality. Experiments on three cross-modality face synthesis tasks (NIR-to-VIS, RGB-to-depth, and sketch-to-photo) show the effectiveness of the proposed approach compared with the state-of-the-art. In addition, we also collect a large-scale RGB-D dataset (VIPL-MumoFace-3K) for the RGB-to-depth synthesis task. We plan to open-source our code and VIPL-MumoFace-3K dataset to the community (https://github.com/skgyu/CMOS-GAN).

Two-stage person search methods achieve the state-of-the-art performance by separate detection and re-ID stages, but neglect the consistency needs between these two stages. The re-ID stage needs more accurate query bounding boxes and fewer boxes of distractors; The detection stage needs the re-ID stage to have robustness against unavailable detection errors. In this paper, we introduce a novel Bi-directional Task-Consistent Learning (BTCL) person search framework, including a Target-Specific Detector (TSD) and a re-ID model with Dynamic Adaptive Learning Structure (DALS). For the former consistency need, we add a verification head for predicting the similarity scores between query and proposals in parallel with the existing heads for bounding box recognition. Thus, TSD generates accurate boxes for the query-like pedestrians, which are suitable for the re-ID stage. For the re-ID robustness need, DALS dynamically generates a large number of possible detection results in line with the real distribution. By training the re-ID model on data with different types of detection errors, DLAS improves the model robustness to detection inputs. Experimental results show our framework achieves state-of-the-art performance on two widely-used person search datasets.

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we manually label the eigenfunction signs for a variety of shapes and train a novel neural network to predict the sign of each eigenfunction under symmetry. Our network aims at learning the global property of functions and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated bases, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 20 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.

Due to the limited utilization of temporal relations in video re-id, the frame-level attention regions of mainstream methods are partial and highly similar. To address this problem, we propose a Salient-to-Broad Module (SBM) to enlarge the attention regions gradually. Specifically, in SBM, while the previous frames have focused on the most salient regions, the later frames tend to focus on broader regions. In this way, the additional information in broad regions can supplement salient regions, incurring more powerful video-level representations. To further improve SBM, an Integration-and-Distribution Module (IDM) is introduced to enhance frame-level representations. IDM first integrates features from the entire feature space and then distributes the integrated features to each spatial location. SBM and IDM are mutually beneficial since they enhance the representations from video-level and frame-level, respectively. Extensive experiments on four prevalent benchmarks demonstrate the effectiveness and superiority of our method. The source code is available at https://github.com/baist/SINet.

The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multi-modality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at: h t t$p$s: //github.com/guxinqian/Simple-CCReID.

Attributed to both the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of uncontrolled illumination and pose. In this paper, we propose to enhance face recognition with a bypass of self-supervised 3D reconstruction, which enforces the neural backbone to focus on the identity-related depth and albedo information while neglects the identity-irrelevant pose and illumination information. Specifically, inspired by the physical model of image formation, we improve the backbone FR network by introducing a 3D face reconstruction loss with two auxiliary networks. The first one estimates the pose and illumination from the input face image while the second one decodes the canonical depth and albedo from the intermediate feature of the FR backbone network. The whole network is trained in end-to-end manner with both classic face identification loss and the loss of 3D face reconstruction with the physical parameters. In this way, the self-supervised reconstruction acts as a regularization that enables the recognition network to understand faces in 3D view, and the learnt features are forced to encode more information of canonical facial depth and albedo, which is more intrinsic and beneficial to face recognition. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art ones. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.

In this paper, we propose a novel gradual domain adaptation method with sample transferability exploitation to tackle the unsupervised domain adaptation (UDA) for person re-identification (re-id). Due to the direct but rough adaptation scheme, existing UDA for person re-id methods usually suffer from source domain-specific characteristics. To filter out the source domain-specific characteristics, motivated by the curriculum learning strategy, we conduct gradual domain adaptation by domain-level re-weighting with polynomial weight decay. Furthermore, we exploit sample transferability via maximum mean discrepancy based sample-level re-weighting strategy to diminish the domain gap. The sample transferability exploitation spotlights samples with higher importance to the adaptation process in each domain, hence enhance the adaptation performance. By combining the gradual domain adaptation with the sample transferability exploitation, our method achieves the state-of-the-art performance on transferring between two common person re-id datasets.

Lip reading, also known as visual speech recognition (VSR), is the task to recognize the speech content using only the visual modality. Inspired by the natural synchronization between acoustic speech signal and the speaker’s facial movements in the speaking process, some methods have begun to introduce the auditory modality to help the learning process of the lip reading models, especially by distilling knowledge from the audio speech recognition models to the lip reading models. However, existing works usually overlook the domain gap between the audio and visual modalities, which greatly limits the ability of the lip reading models to learn speech-related information from the audio modality and so further hinder the improvement of the lip reading models for the VSR task. In this paper, we aim to establish a bridge between the audio modality and the visual modality for the lip reading model to learn more effectively from the audio modality. Specifically, we introduce the audio-driven deformation flow to reflect the potential visual dynamics corresponding with the acoustic speech signal. The generated deformation flow is directly decided by the input acoustic speech signals and so focuses more on the facial dynamics corresponding with the speech signal, rather than the unrelated visual conditions, like illumination, pose, skin color, and so on. This property makes the flow-based model more effective as the teacher than the usual ASR models for the lip reading task. With this basic idea, we propose an encoder-decoder architecture to generate the deformation flow and distill the speech-related knowledge from the deformation flow-based VSR model to the lip reading models, instead of from the ASR models directly. Finally, we evaluate our method on two popular large-scale lip reading datasets, LRW and LRS2-BBC, respectively. The results show that our method can not only improve the lip reading model’s performance without extra computation cost at the test phase, but also achieve higher performance than distilling from the ASR model directly which shows the advantages of the proposed deformation flow based method.

Context information is important for instance segmentation on point clouds. Existing methods either only use local surroundings by stacking multiple convolution layers or use non-local methods to model long-range interactions. However, they usually directly operate on points which is an unstructured and low-level representation and is highly dependent on context. To address this issue, we propose an effective framework named Implicit-Part Context Aggregation (IPCA), which adopts implicit parts as an intermediate representation and achieves context aggregation through message passing along the implicit part graph. Specifically, we first organize unstructured points into geometrically consistent implicit parts and construct the implicit part graph according to the geometric adjacency. Then, an initial part embedding is extracted using the proposed Implicit Part Network (IPN) which can aggregate point features and capture the intrinsic geometric shape of the part. We further refine the part embedding by a graph reasoning module named Context Aggregation Network (CAN), which helps to make a more precise prediction by well exploiting the context information. Instance proposals are then generated by grouping implicit parts. Finally, we propose an additional step to attribute the entire instance proposal to a Semantic Criterion Net (SCN) to infer the semantics of the instance. The purpose is to correct the semantic prediction errors caused by not knowing the boundary and overall shape of the object in the previous steps. Extensive experiments on two large datasets, ScanNet and 3RScan, demonstrate the effectiveness of our method. To our knowledge, it yields the highest performance on the ScanNet test benchmark and its AP@50 is 9.5 points higher than the baseline. The code is available at https://github.com/xiaodongww/IPCA

Contrastive learning, which learns to contrast positive with negative pairs of samples, has been popular for self-supervised visual representation learning. Although great effort has been made to design proper positive pairs through data augmentation, few works attempt to generate optimal positives for each instance. Inspired by semantic consistency and computational advantage in latent space of pretrained generative models, this paper proposes to learn instance-specific latent transformations to generate Contrastive Optimal Positives (COP-Gen) for self-supervised contrastive learning. Specifically, we formulate COP-Gen as an instance-specific latent space navigator which minimizes the mutual information between the generated positive pair subject to the semantic consistency constraint. Theoretically, the learned latent transformation creates optimal positives for contrastive learning, which removes as much nuisance information as possible while preserving the semantics. Empirically, using generated positives by COP-Gen consistently outperforms other latent transformation methods and even real-image-based methods in self-supervised contrastive learning.

Temporal Video Segmentation (TVS) is a fundamental video understanding task and has been widely researched in recent years. There are two subtasks of TVS: Video Action Segmentation (VAS) and Video Procedure Segmentation (VPS): VAS aims to recognize what actions happen in-side the video while VPS aims to segment the video into a sequence of video clips as a procedure. The VAS task inevitably relies on pre-defined action labels and is thus hard to scale to various open-domain videos. To overcome this limitation, the VPS task tries to divide a video into several category-independent procedure segments. However, the existing dataset for the VPS task is small (2k videos) and lacks diversity (only cooking domain). To tackle these problems, we collect a large and diverse dataset called TIPS, specifically for the VPS task. TIPS contains 63k videos including more than 300k procedure segments from instructional videos on YouTube, which covers plenty of how-to areas such as cooking, health, beauty, parenting, gardening, etc. We then propose a multi-modal Transformer with Gaussian Boundary Detection (MT-GBD) model for VPS, with the backbone of the Transformer and Convolution. Furthermore, we propose a new EIOU metric for the VPS task, which helps better evaluate VPS quality in a more comprehensive way. Experimental results show the effectiveness of our proposed model and metric.

Zero-Shot Detection (ZSD), which aims at localizing and recognizing unseen objects in a complicated scene, usually leverages the visual and semantic information of individual objects alone. However, scene understanding of human exceeds recognizing individual objects separately: the contextual information among multiple objects such as visual relational information (e.g. visually similar objects) and semantic relational information (e.g. co-occurrences) is helpful for understanding of visual scene. In this paper, we verify that contextual information plays a more important role in ZSD than in traditional object detection. To make full use of such information, we propose a new end-to-end ZSD method GRaph Aligning Network (GRAN) based on graph modeling and reasoning which simultaneously considers visual and semantic information of multiple objects instead of individual objects. Specifically, we formulate a Visual Relational Graph (VRG) and a Semantic Relational Graph (SRG), where the nodes are the objects in the image and the semantic representations of classes respectively and the edges are the relevance between nodes in each graph. To characterize mutual effect between two modalities, the two graphs are further merged into a heterogeneous Visual-Semantic Relational Graph (VSRG), where modal translators are designed for the two subgraphs to enable modal information to transform into a common space for communication, and message passing among nodes is enforced to refine their representations. Comprehensive experiments on MSCOCO dataset demonstrate the advantage of our method over state-of-the-arts, and qualitative analysis suggests the validity of using contextual information.

Multi-Source Domain Adaptation (MSDA) aims at transferring knowledge from multiple labeled source domains to benefit the task in an unlabeled target domain. The challenges of MSDA lie in mitigating domain gaps and combining information from diverse source domains. In most existing methods, the multiple source domains can be jointly or separately aligned to the target domain. In this work, we consider that these two types of methods, i.e. joint and separate domain alignments, are complementary and propose a mutual learning based alignment network (MLAN) to combine their advantages. Specifically, our proposed method is composed of three components, i.e. a joint alignment branch, a separate alignment branch, and a mutual learning objective between them. In the joint alignment branch, the samples from all source domains and the target domain are aligned together, with a single domain alignment goal, while in the separate alignment branch, each source domain is individually aligned to the target domain. Finally, by taking advantage of the complementarity of joint and separate domain alignment mechanisms, mutual learning is used to make the two branches learn collaboratively. Compared with other existing methods, our proposed MLAN integrates information of different domain alignment mechanisms and thus can mine rich knowledge from multiple domains for better performance. The experiments on Domain-Net, Office-31, and Digits-five datasets demonstrate the effectiveness of our method.

Teaching machines to recognize a new category based on few training samples especially only one remains challenging owing to the incomprehensive understanding of the novel category caused by the lack of data. However, human can learn new classes quickly even given few samples since human can tell what discriminative features should be focused on about each category based on both the visual and semantic prior knowledge. To better utilize those prior knowledge, we propose the SEmantic Guided Attention (SEGA) mechanism where the semantic knowledge is used to guide the visual perception in a top-down manner about what visual features should be paid attention to when distinguishing a category from the others. As a result, the embedding of the novel class even with few samples can be more discriminative. Concretely, a feature extractor is trained to embed few images of each novel class into a visual prototype with the help of transferring visual prior knowledge from base classes. Then we learn a network that maps semantic knowledge to category-specific attention vectors which will be used to perform feature selection to enhance the visual prototypes. Extensive experiments on miniImageNet, tieredImageNet, CIFAR-FS, and CUB indicate that our semantic guided attention realizes anticipated function and outperforms state-of-the-art results.

Person re-identification (reID) plays an important role in computer vision. However, existing methods suffer from performance degradation in occluded scenes. In this work, we propose an occlusion-robust block, Region Feature Completion (RFC), for occluded reID. Different from most previous works that discard the occluded regions, RFC block can recover the semantics of occluded regions in feature space. First, a Spatial RFC (SRFC) module is developed. SRFC exploits the long-range spatial contexts from non-occluded regions to predict the features of occluded regions. The unit-wise prediction task leads to an encoder/decoder architecture, where the region-encoder models the correlation between non-occluded and occluded region, and the region-decoder utilizes the spatial correlation to recover occluded region features. Second, we introduce Temporal RFC (TRFC) module which captures the long-term temporal contexts to refine the prediction of SRFC. RFC block is lightweight, end-to-end trainable and can be easily plugged into existing CNNs to form RFCnet. Extensive experiments are conducted on occluded and commonly holistic reID benchmarks. Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets. The source code is available at https://github.com/blue-blue272/OccludedReID-RFCnet.

Capturing long-range dependencies during feature extraction is crucial for video-based person re-identification (re-id) since it would help to tackle many challenging problems such as occlusion and dramatic pose variation. Moreover, capturing subtle differences, such as bags and glasses, is indispensable to distinguish similar pedestrians. In this paper, we propose a novel and efficacious Statistic Attention (SA) block which can capture both the long-range dependencies and subtle differences. SA block leverages high-order statistics of feature maps, which contain both long-range and high-order information. By modeling relations with these statistics, SA block can explicitly capture long-range dependencies with less time complexity. In addition, high-order statistics usually concentrate on details of feature maps and can perceive the subtle differences between pedestrians. In this way, SA block is capable of discriminating pedestrians with subtle differences. Furthermore, this lightweight block can be conveniently inserted into existing deep neural networks at any depth to form Statistic Attention Network (SANet). To evaluate its performance, we conduct extensive experiments on two challenging video re-id datasets, showing that our SANet outperforms the state-of-the-art methods. Furthermore, to show the generalizability of SANet, we evaluate it on three image re-id datasets and two more general image classification datasets, including ImageNet. The source code is available athttp://vipl.ict.ac.cn/resources/codes/code/SANet_code.zip.

Apathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the patient’s presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both, patients and clinical staff, hindering among other large-scale diagnostics. In this work, we propose a novel spatio-temporal framework for apathy classification, which is streamlined to analyze facial dynamics and emotion in videos. Specifically, we divide the videos into smaller clips, and proceed to extract associated facial dynamics and emotion-based features. Statistical representations/descriptors based on each feature and clip serve as input of the proposed Gated Recurrent Unit (GRU)-architecture. Temporal representations of individual features at the lower level of the proposed architecture are combined at deeper layers of the proposed GRU architecture, in order to obtain the final feature-set for apathy classification. Based on extensive experiments, we show that fusion of characteristics such as emotion and facial dynamics in proposed deep-bi-directional GRU obtains an accuracy of 95.34% in apathy classification.

Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the training images with artificial occlusions has been proved as a useful approach. However, these artificial occlusions are commonly generated by adding a black rectangle or several object templates including sunglasses, scarfs and phones, which cannot well simulate the realistic occlusions. In this paper, based on the argument that the occlusion essentially damages a group of neurons, we propose a novel and elegant occlusion-simulation method via dropping the activations of a group of neurons in some elaborately selected channel. Specifically, we first employ a spatial regularization to encourage each feature channel to respond to local and different face regions. Then, the locality-aware channel-wise dropout (LCD) is designed to simulate occlusions by dropping out a few feature channels. The proposed LCD can encourage its succeeding layers to minimize the intra-class feature variance caused by occlusions, thus leading to improved robustness against occlusion. In addition, we design an auxiliary spatial attention module by learning a channel-wise attention vector to reweight the feature channels, which improves the contributions of non-occluded regions. Extensive experiments on various benchmarks show that the proposed method outperforms state-of-the-art methods with a remarkable improvement.

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data. To utilize the topology information while staying robust under different triangulations, we propose to encode mesh connectivity using Laplacian spectral analysis, along with mesh feature aggregation blocks (MFABs) that can split the surface domain into local pooling patches and aggregate global information amongst them. We build a mesh hierarchy from fine to coarse using Laplacian spectral clustering, which is flexible under isometric transformations. Inside the MFABs there are pooling layers to collect local information and multi-layer perceptrons to compute vertex features of increasing complexity. To obtain the relationships among different clusters, we introduce a Correlation Net to compute a correlation matrix, which can aggregate the features globally by matrix multiplication with cluster features. Our network architecture is flexible enough to be used on meshes with different numbers of vertices. We conduct several experiments including shape segmentation and classification, and our method outperforms state-of-the-art algorithms for these tasks on the ShapeNet and COSEG datasets.

Lei Ji, Xianglin Guo, Haoyang Huang, Xilin Chen. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Image caption evaluation is a crucial task, which involves the semantic perception and matching of image and text. Good evaluation metrics aim to be fair, comprehensive, and consistent with human judge intentions. When humans evaluate a caption, they usually consider multiple aspects, such as whether it is related to the target image without distortion, how much image gist it conveys, as well as how fluent and beautiful the language and wording is. The above three different evaluation orientations can be summarized as fidelity, adequacy, and fluency. The former two rely on the image content, while fluency is purely related to linguistics and more subjective. Inspired by human judges, we propose a learning-based metric named FAIEr to ensure evaluating the fidelity and adequacy of the captions. Since image captioning involves two different modalities, we employ the scene graph as a bridge between them to represent both images and captions. FAIEr mainly regards the visual scene graph as the criterion to measure the fidelity. Then for evaluating the adequacy of the candidate caption, it high-lights the image gist on the visual scene graph under the guidance of the reference captions. Comprehensive experimental results show that FAIEr has high consistency with human judgment as well as high stability, low reference dependency, and the capability of reference-free evaluation.

Set-based Face Recognition is widely applied in scenarios like law enforcement and online media data management. Compared with face recognition using a single image, the faces in the set often contain abundant appearance changes. Therefore, how to make full use of the rich information from the set and integrate them into a unified set representation become the key to set-based face recognition. Inspired by the fact that humans usually complete this fine-grained task through integrating the information from the congruent local regions (e.g. an eye to an eye) of multiple faces in a set, we propose a novel method called Local Feature Enhancement Network (LFENet), which can automatically enhance the local feature through transferring the local information across the images. Specifically, we retain the spatial semantic information of the feature maps and apply different relational functions to establish the correlation among the local features. The contained local information will be transferred to the relevant local features to enhance their discriminability. By doing so, the valuable local information carried in some local features can complement those with incomplete information. Besides, the various local information is aligned across faces under different conditions to help the model learn intra-set-compact face representations. Our method achieves state-of-the-art performances on two mainstream set-based face recognition benchmarks: IJB-A and IJB-C, which fully reflects the rationality and effectiveness of our local feature enhancement mechanism.

In this paper, we propose a new method for remote photoplethysmography (rPPG) based heart rate (HR) estimation. In particular, our proposed method BVPNet is streamlined to predict the blood volume pulse (BVP) signals from face videos. Towards this, we firstly define ROIs based on facial landmarks and then extract the raw temporal signal from each ROI. Then the extracted signals are pre-processed via first-order difference and Butterworth filter and combined to form a Spatial-Temporal map (STMap). We then propose to revise U-Net, in order to predict BVP signals from the STMap. BVPNet takes into account both temporal and frequency domain losses in order to learn better than conventional models. Our experimental results suggest that our BVPNet outperforms the state-of-the-art methods on two publicly available datasets (MMSE-HR and VIPL-HR).

Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects’ state changes, which are still challenging for current AI systems. To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. Env-QA contains 23K egocentric videos, where each video is composed of a series of events about exploring and interacting in the environment. It also provides 85K questions to evaluate the ability of understanding the composition, layout, and state changes of the environment presented by the events in videos. Moreover, we propose a video QA model, Temporal Segmentation and Event Attention network (TSEA), which introduces event-level video representation and corresponding attention mechanisms to better extract environment information and answer questions. Comprehensive experiments demonstrate the effectiveness of our framework and show the formidable challenges of Env-QA in terms of long-term state tracking, multi-event temporal reasoning and event counting, etc.

In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network. However, due to the limitation of chain rules in back-propagation, the visual module is hard to adjust for seeking optimized visual features. As a result, it enforces that the contextual module focuses on contextual information optimization only rather than balancing efficient visual and contextual information. In this paper, we propose a Self-Mutual Knowledge Distillation (SMKD) method, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously. Specifically, the visual and contextual modules share the weights of their corresponding classifiers, and train with CTC loss simultaneously. Moreover, the spike phenomenon widely exists with CTC loss. Although it can help us choose a few of the key frames of a gloss, it does drop other frames in a gloss and makes the visual feature saturation in the early stage. A gloss segmentation is developed to relieve the spike phenomenon and decrease saturation in the visual module. We conduct experiments on two CSLR bench-marks: PHOENIX14 and PHOENIX14-T. Experimental results demonstrate the effectiveness of the SMKD.

Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented signs from image streams. Overfitting is one of the most critical problems in CSLR training, and previous works show that the iterative training scheme can partially solve this problem while also costing more training time. In this study, we revisit the iterative training scheme in recent CSLR works and realize that sufficient training of the feature extractor is critical to solving the overfitting problem. Therefore, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with alignment supervision. Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module. Moreover, we propose two metrics to reflect overfitting by measuring the prediction inconsistency between the feature extractor and the alignment module. Experimental results on two challenging CSLR datasets show that the proposed VAC makes CSLR networks end-to-end trainable and achieves competitive performance.

In order to train 3D gaze estimators without too many annotations, we propose an unsupervised learning framework, Cross-Encoder, to leverage the unlabeled data to learn suitable representation for gaze estimation. To address the issue that the feature of gaze is always intertwined with the appearance of the eye, Cross-Encoder disentangles the features using a latent-code-swapping mechanism on eye-consistent image pairs and gaze-similar ones. Specifically, each image is encoded as a gaze feature and an eye feature. Cross-Encoder is trained to reconstruct each image in the eye-consistent pair according to its gaze feature and the other’s eye feature, but to reconstruct each image in the gaze-similar pair according to its eye feature and the other’s gaze feature. Experimental results show the validity of our work. First, using the Cross-Encoder-learned gaze representation, the gaze estimator trained with very few samples outperforms the ones using other unsupervised learning methods, under both within-dataset and cross-dataset protocol. Second, ResNet18 pretrained by Cross-Encoder is competitive with state-of-the-art gaze estimation methods. Third, ablation study shows that Cross-Encoder disentangles the gaze feature and eye feature.

If an image tells a story, the image caption is the briefest narrator. Generally, a scene graph prefers to be an omniscient "generalist", while the image caption is more willing to be a "specialist", which outlines the gist. Lots of previous studies have found that a scene graph is not as practical as expected unless it can reduce the trivial contents and noises. In this respect, the image caption is a good tutor. To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the socalled Topic Scene Graph. What an image caption pays attention to is distilled and passed to the scene graph for estimating the importance of partial objects, relationships, and events. Specifically, during the caption generation, the attention about individual objects in each time step is collected, pooled, and assembled to obtain the attention about relationships, which serves as weak supervision for regularizing the estimated importance scores of relationships. In addition, as this attention distillation process provides an opportunity for combining the generation of image caption and scene graph together, we further transform the scene graph into linguistic form with rich and free-form expressions by sharing a single generation model with image caption. Experiments show that attention distillation brings significant improvements in mining important relationships without strong supervision, and the topic scene graph shows great potential in subsequent applications.

Due to the missing depth cues, it is essentially ambiguous to detect 3D objects from a single RGB image. Existing methods predict the 3D pose for each object independently or merely by combining local relationships within limited surroundings, but rarely explore the inherent geometric relationships from a global perspective. To address this issue, we argue that modeling geometric structure among objects in a scene is very crucial, and thus elaborately devise the Holistic Pose Graph (HPG) that explicitly integrates all geometric poses including the object pose treated as nodes and the relative pose treated as edges. The inference of the HPG uses GRU to encode the pose features from their corresponding regions in a single RGB image, and passes messages along the graph structure iteratively to improve the predicted poses. To further enhance the correspondence between the object pose and the relative pose, we propose a novel consistency loss to explicitly measure the deviations between them. Finally, we apply Holistic Pose Estimation (HPE) to jointly evaluate both the independent object pose and the relative pose. Our experiments on the SUN RGB-D dataset demonstrate that the proposed method provides a significant improvement on 3D object prediction.

We propose a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD). Traditional methods for ASD usually operate on each candidate's pre-cropped face track separately and do not sufficiently consider the relationships among the candidates. This potentially limits performance, especially in challenging scenarios with low-resolution faces, multiple candidates, etc. Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties. Based on such information, our model optimizes all candidates in a unified process for robust and reliable ASD. A thorough ablation study is performed on several challenging ASD benchmarks under different settings. In particular, our method outperforms the state-of-the-art by a large margin of about 15% mean Average Precision (mAP) absolute on two challenging subsets: one with three candidate speakers, and the other with faces smaller than 64 pixels. Together, our UniCon achieves 92.0% mAP on the AVA-ActiveSpeaker validation set, surpassing 90% for the first time on this challenging dataset at the time of submission. Project website: https://unicon-asd.github.io/.

We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost. We take advantage of the multi-resolution parallel design introduced in high-resolution convolutional networks (HRNet [45]), along with local-window self-attention that performs self-attention over small non-overlapping image windows [21], for improving the memory and computation efficiency. In addition, we introduce a convolution into the FFN to exchange information across the disconnected image windows. We demonstrate the effectiveness of the HighResolution Transformer on both human pose estimation and semantic segmentation tasks, e.g., HRFormer outperforms Swin transformer [27] by 1.3 AP on COCO pose estimation with 50% fewer parameters and 30% fewer FLOPs. Code is available at: https://github.com/HRNet/HRFormer

State-of-the-art classification models are usually considered as black boxes since their decision processes are implicit to humans. On the contrary, human experts classify objects according to a set of explicit hierarchical criteria. For example, "tabby is a domestic cat with stripes, dots, or lines", where tabby is defined by combining its superordinate category (domestic cat) and some certain attributes (e.g., has stripes). Inspired by this mechanism, we propose an interpretable Hierarchical Criteria Network (HCN) by additionally learning such criteria. To achieve this goal, images and semantic entities (e.g., taxonomies and attributes) are embedded into a common space, where each category can be represented by the linear combination of its superordinate category and a set of learned discriminative attributes. Specifically, a two-stream convolutional neural network (CNN) is elaborately devised, which embeds images and taxonomies with the two streams respectively. The model is trained by minimizing the prediction error of hierarchy labels on both streams. Extensive experiments on two widely studied datasets (CIFAR-100 and ILSVRC) demonstrate that HCN can learn meaningful attributes as well as reasonable and interpretable classification criteria. Therefore, the proposed method enables further human feedback for model correction as an additional benefit.

Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.

Location is an important distinguishing information for instance segmentation. In this paper, we propose a novel model, called Location Sensitive Network (LSNet), for human instance segmentation. LSNet integrates instance-specific location information into one-stage segmentation framework. Specifically, in the segmentation branch, Pose Attention Module (PAM) encodes the location information into the attention regions through coordinates encoding. Based on the location information provided by PAM, the segmentation branch is able to effectively distinguish instances in feature-level. Moreover, we propose a combination operation named Keypoints Sensitive Combination (KSCom) to utilize the location information from multiple sampling points. These sampling points construct the points representation for instances via human keypoints and random points. Human keypoints provide the spatial locations and semantic information of the instances, and random points expand the receptive fields. Based on the points representation for each instance, KSCom effectively reduces the mis-classified pixels. Our method is validated by the experiments on public datasets. LSNet-5 achieves 56.2 mAP at 18.5 FPS on COCOPersons. Besides, the proposed method is significantly superior to its peers in the case of severe occlusion.

Person reidentification (reID) by convolutional neural network (CNN)-based networks has achieved favorable performance in recent years. However, most of existing CNN-based methods do not take full advantage of spatial-temporal context modeling. In fact, the global spatial-temporal context can greatly clarify local distractions to enhance the target feature representation. To comprehensively leverage the spatial-temporal context information, in this work, we present a novel block, interaction-aggregation-update (IAU), for high-performance person reID. First, the spatial-temporal IAU (STIAU) module is introduced. STIAU jointly incorporates two types of contextual interactions into a CNN framework for target feature learning. Here, the spatial interactions learn to compute the contextual dependencies between different body parts of a single frame, while the temporal interactions are used to capture the contextual dependencies between the same body parts across all frames. Furthermore, a channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts. Therefore, the IAU block enables the feature to incorporate the globally spatial, temporal, and channel context. It is lightweight, end-to-end trainable, and can be easily plugged into existing CNNs to form IAUnet. The experiments show that IAUnet performs favorably against state of the art on both image and video reID tasks and achieves compelling results on a general object categorization task. The source code is available at https://github.com/blue-blue272/ImgReID-IAnet.

There is a large group of deaf-mutes in the world, and sign language is their major communication tool. Therefore, it’s necessary for the deaf-mutes to communicate with hearing-speech people and the hearing-speech people also have needed to understand sign language, which produces a great demand for sign language teaching. Even though there have already been a large number of books for sign language, it is low efficient to learn sign language with books, even teaching videos. To solve this problem, we develop a smartphone-based interactive Chinese sign language teaching systemfor sign language learning. The system provides a learner with some kinds of learning modes and captures the learner’s actions from its front camera of the smartphone. Right now the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary and the system will turn the top three retrieved candidates, so it can remind the learners what the sign is. This system provides interactive learning for a user to learn sign language high efficiently. The systemadopts an algorithm based on point cloud recognition to evaluate a user’s sign and costs about 700ms inference time for each sample, which meets the real-time requirements. This interactive learning system decreases the communication barriers between the deaf-mutes and hearing-speechers.

Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of places, products, and sports teams. To overcome this difficulty, only resorting to pre-trained word embedding models is far from enough. A desired model should utilize the rich information in multiple modalities of the image to help understand the meaning of scene texts, e.g., the prominent text on a bottle is most likely to be the brand. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). It first represents an image as a graph consisting of three sub-graphs, depicting visual, semantic, and numeric modalities respectively. Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes. The updated nodes have better features for the downstream question answering module. Experimental evaluations show that our MM-GNN represents the scene texts better and obviously facilitates the performances on two VQA tasks that require reading scene texts.

Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-invariant representations of two domains should share the same characteristics from perspective of the overview and also any local piece. Inspired by this, we propose a novel method called Hierarchical Gradient Synchronization to model the synchronization relationship among the local distribution pieces and global distribution, aiming for more precise domain-invariant features. Specifically, the hierarchical domain alignments including class-wise alignment, group-wise alignment and global alignment are first constructed. Then, these three types of alignment are constrained to be consistent to ensure better structure preservation. As a result, the obtained features are domain invariant and intrinsically structure preserved. As evaluated on extensive domain adaptation tasks, our proposed method achieves state-of-the-art classification performance on both vanilla unsupervised domain adaptation and partial domain adaptation.

Existing domain generalization methods for face anti-spoofing endeavor to extract common differentiation features to improve the generalization. However, due to large distribution discrepancies among fake faces of different domains, it is difficult to seek a compact and generalized feature space for the fake faces. In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing. The main idea is to learn a generalized feature space, where the feature distribution of the real faces is compact while that of the fake ones is dispersed among domains but compact within each domain. Specifically, a feature generator is trained to make only the real faces from different domains undistinguishable, but not for the fake ones, thus forming a single-side adversarial learning. Moreover, an asymmetric triplet loss is designed to constrain the fake faces of different domains separated while the real ones aggregated. The above two points are integrated into a unified framework in an end-to-end training manner, resulting in a more generalized class boundary, especially good for samples from novel domains. Feature and weight normalization is incorporated to further improve the generalization ability. Extensive experiments show that our proposed approach is effective and outperforms the state-of-the-art methods on four public databases. The code is released online.

Point clouds contain rich spatial information, which provides complementary cues for gesture recognition. In this paper, we formulate gesture recognition as an irregular sequence recognition problem and aim to capture long-term spatial correlations across point cloud sequences. A novel and effective PointLSTM is proposed to propagate information from past to future while preserving the spatial structure. The proposed PointLSTM combines state information from neighboring points in the past with current features to update the current states by a weight-shared LSTM layer. This method can be integrated into many other sequence learning approaches. In the task of gesture recognition, the proposed PointLSTM achieves state-of-the-art results on two challenging datasets (NVGesture and SHREC'17) and outperforms previous skeleton-based methods. To show its advantages in generalization, we evaluate our method on MSR Action3D dataset, and it produces competitive results with previous skeleton-based methods.

Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.

The state of the art person search methods separate person search into detection and re-ID stages, but ignore the consistency between these two stages. The general person detector has no special attention on the query target; The re-ID model is trained on hand-drawn bounding boxes which are not available in person search. To address the consistency problem, we introduce a Task-Consist Two-Stage (TCTS) person search framework, includes an identity-guided query (IDGQ) detector and a Detection Results Adapted (DRA) re-ID model. In the detection stage, the IDGQ detector learns an auxiliary identity branch to compute query similarity scores for proposals. With consideration of the query similarity scores and foreground score, IDGQ produces query-like bounding boxes for the re-ID stage. In the re-ID stage, we predict identity labels of detected bounding boxes, and use these examples to construct a more practical mixed train set for the DRA model. Training on the mixed train set improves the robustness of the re-ID stage to inaccurate detection. We evaluate our method on two benchmark datasets, CUHK-SYSU and PRW. Our framework achieves 93.9% of mAP and 95.1% of rank1 accuracy on CUHK-SYSU, outperforming the previous state of the art methods.

Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision. The code is released online.

Human poses play important roles in action analysis. However, most state-of-the-art approaches in action recognition ignore the importance of human poses and rarely leverage the pose information for further improving the recognition performance. In this paper, we propose a novel network architecture, which simultaneously considers the appearance information and pose knowledge for robust action recognition. We explore various architectures for fusing the appearance and pose information rather than simply averaging scores at the final layer. Moreover, a novel training strategy is proposed to reduce the influence of overfitting for limited training data. Extensive experiments show that our method achieves competitive performance on the popular benchmarks, i.e., UCF-101 and HMDB-51.

Lip-reading aims to infer the speech content from the lip movement sequence and can be seen as a typical sequence-to-sequence (seq2seq) problem which translates the input image sequence of lip movements to the text sequence of the speech content. However, the traditional learning process of seq2seq models always suffers from two problems: the exposure bias resulted from the strategy of “teacher-forcing”, and the inconsistency between the discriminative optimization target (usually the cross-entropy loss) and the final evaluation metric (usually the character/word error rate). In this paper, we propose a novel pseudo-convolutional policy gradient (PCPG) based method to address these two problems. On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target. On the other hand, inspired by the local perception property of convolutional operation, we perform a pseudo-convolutional operation on the reward and loss dimension, so as to take more context around each time step into account to generate a robust reward and loss for the whole optimization. Finally, we perform a thorough comparison and evaluation on both the word-level and sentence-level benchmarks. The results show a significant improvement over other related methods, and report either a new state-of-the-art performance or a competitive accuracy on all these challenging benchmarks, which clearly proves the advantages of our approach.

Video face retrieval (VFR) is an appealing and practical computer vision task, which aims to search particular character from masses of videos like in TV-Series. The challenges of this task mainly lie in two aspects, i.e. faces in such videos contain complex appearance variations with uncontrollable shooting environment and searching from big data usually requires high efficiency in both space and time. To fulfill this task, current works typically proceed in a learning to hash manner by fusing single-frame features within a video to obtain the video representation and further embedding it into Hamming space to yield video binary codes. The feature fusion stage has inevitably discarded too much frame information and leads to less discriminative video codes. In this paper, we propose Hybrid Video and Image Hashing (HVIH) to learn more effective binary codes for face videos. Specifically, we fully exploit the dense frame features rather than simply discarding them after the video level fusion and jointly optimize binary codes for the video and its composed frames in adapted supervised manners. To achieve more robust video representation, we introduce a module of video center alignment to ensure the binary codes location of the video and its frames to be as compact and consistent as possible in the Hamming space, which naturally facilitates both tasks of video-to-video retrieval and image-to-video retrieval. Extensive experiments on two challenging video face databases demonstrate the superiority of our approach over the state-of-the-art.

Lip reading is the task of recognizing speech content by analyzing movements in the lip region when people are speaking. Based on the continuity in adjacent frames in the speaking process, and the consistency in motion patterns among different people when they pronounce the same phoneme, we model lip movements as a sequence of apparent deformations in the lip region during the speaking process. Specifically, we introduce a Deformation Flow Network (DFN) to learn the deformation flow between adjacent frames, which directly captures the motion information within the lip region. The learned deformation flow is then combined with the original grayscale frames with a two-stream network to perform lip reading. To make the two streams learn from each other in the learning process, we introduce a bidirectional knowledge distillation loss to train the two branches jointly. Owing to the complementary cues provided by different branches, the two-stream network shows substantial improvement over using either single branch. A thorough experimental evaluation on two large-scale lip reading benchmarks is presented with detailed analysis. The results accord with our motivation, and show that our method achieves state-of-the-art or comparable performance on these two challenging datasets.

Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR). Currently, most existing methods equate VSR with automatic lip reading, which attempts to recognise speech by analysing lip motion. However, human experience and psychological studies suggest that we do not always fix our gaze at each other's lips during a face-to-face conversation, but rather scan the whole face repetitively. This inspires us to revisit a fundamental yet somehow overlooked problem: can VSR models benefit from reading extraoral facial regions, i.e. beyond the lips? In this paper, we perform a comprehensive study on the evaluation of the effects of different facial regions with state-of-the-art VSR models, including the mouth, the whole face, the upper face, and even the cheeks. Experiments are conducted on both word-level and sentence-level benchmarks with different characteristics. We find that despite the complex variations of the data, incorporating information from extraoral facial regions, even the upper face, consistently benefits VSR performance. Furthermore, we introduce a simple yet effective method based on Cutout to learn more discriminative features for face-based VSR, hoping to maximise the utility of information encoded in different facial regions. Our experiments show obvious improvements over existing state-of-the-art methods that use only the lip region as inputs, a result we believe would probably provide the VSR community with some new and exciting insights.

Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be used to capture the lip movement information and meanwhile to resist the noises resulted by the change of pose, lighting conditions, speaker's appearance, speaking speed and so on. Towards this target, we propose to introduce the mutual information constraints on both the local feature's level and the global sequence's level to enhance the relations of them with the speech content. On the one hand, we require the features generated at each time step to carry a strong relation with the speech content by imposing the local mutual information maximization constraint (LMIM), so as to improve the model's ability to discover fine-grained lip movements and the finegrained differences between words with similar pronunciation, such as “spend” and “spending”. On the other hand, we introduce the mutual information maximization constraint on the global sequence's level (GMIM), to make the model be able to pay more attention to discriminate key frames related with the speech content, and less to various noises appeared in the speaking process. By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading. To verify this method, we evaluate on two large-scale benchmarks whose videos are collected from several TV shows with a wide coverage of the speaking conditions. We perform a detailed analysis and comparison on several aspects, including the comparison with the baseline of the LMIM and GMIM, and the visualization of the learned representation. The results not only prove the effectiveness of the proposed method but also report new state-of-the-art performance on both the two benchmarks.

Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate 64 × 64, 128 × 128 and 256 × 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.

Video captioning is a fundamental task for visual understanding. Previous works employ end-to-end networks to learn from the low-level vision feature and generate descriptive captions, which are hard to recognize fine-grained objects and lacks the understanding of crucial semantic concepts. According to DPC [19], these concepts generally present in the narrative transcripts of the instructional videos. The incorporation of transcript and video can improve the captioning performance. However, DPC directly concatenates the embedding of transcript with video features, which is incapable of fusing language and vision features effectively and leads to the temporal mis-alignment between transcript and video. This motivates us to 1) learn the semantic concepts explicitly and 2) design a temporal alignment mechanism to better align the video and transcript for the captioning task. In this paper, we start with an encoder-decoder backbone using transformer models. Firstly, we design a semantic concept prediction module as a multi-task to train the encoder in a supervised way. Then, we develop an attention based cross-modality temporal alignment method that combines the sequential video frames and transcript sentences. Finally, we adopt a copy mechanism to enable the decoder(generation) module to copy important concepts from source transcript directly. The extensive experimental results demonstrate the effectiveness of our model, which achieves state-of-the-art results on YouCookII dataset.

Preserving the semantic similarity is one of the most important goals of hashing. Most existing deep hashing methods employ pairs or triplets of samples in training stage, which only consider the semantic similarity within a minibatch and depict the local positional relationship in Hamming space, leading to intermittent semantic similarity preservation. In this paper, we propose Deep Position-Aware Hashing (DPAH) to ensure continuous semantic similarity in Hamming space by modeling global positional relationship. Specifically, we introduce a set of learnable class centers as the global proxies to represent the global information and generate discriminative binary codes by constraining the distance between data points and class centers. In addition, in order to reduce the information loss caused by relaxing the binary codes to real-values in optimization, we propose kurtosis loss (KT loss) to handle the distribution of real-valued features before thresholding to be double-peak, and then enable the real-valued features to be more binarylike. Comprehensive experiments on three datasets show that our DPAH outperforms state-of-the-art methods.

Image-text retrieval of natural scenes has been a popular research topic. Since image and text are heterogeneous cross-modal data, one of the key challenges is how to learn comprehensive yet unified representations to express the multi-modal data. A natural scene image mainly involves two kinds of visual concepts, objects and their relationships, which are equally essential to image-text retrieval. Therefore, a good representation should account for both of them. In the light of recent success of scene graph in many CV and NLP tasks for describing complex natural scenes, we propose to represent image and text with two kinds of scene graphs: visual scene graph (VSG) and textual scene graph (TSG), each of which is exploited to jointly characterize objects and relationships in the corresponding modality. The image-text retrieval task is then naturally formulated as cross-modal scene graph matching. Specifically, we design two particular scene graph encoders in our model for VSG and TSG, which can refine the representation of each node on the graph by aggregating neighborhood information. As a result, both object-level and relationship-level cross-modal features can be obtained, which favorably enables us to evaluate the similarity of image and text in the two levels in a more plausible way. We achieve state-of-the-art results on Flickr30k and MS COCO, which verifies the advantages of our graph matching based approach for image-text retrieval.

Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos; however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HRVIPL-HR is available at: ), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases.

Retrieving videos of a particular person with face image as query via hashing technique has many important applications. While face images are typically represented as vectors in Euclidean space, characterizing face videos with some robust set modeling techniques (e.g. covariance matrices as exploited in this study, which reside on Riemannian manifold), has recently shown appealing advantages. This hence results in a thorny heterogeneous spaces matching problem. Moreover, hashing with handcrafted features as done in many existing works is clearly inadequate to achieve desirable performance for this task. To address such problems, we present an end-toend Deep Heterogeneous Hashing (DHH) method that integrates three stages including image feature learning, video modeling, and heterogeneous hashing in a single framework, to learn unified binary codes for both face images and videos. To tackle the key challenge of hashing on manifold, a well-studied Riemannian kernel mapping is employed to project data (i.e. covariance matrices) into Euclidean space and thus enables to embed the two heterogeneous representations into a common Hamming space, where both intra-space discriminability and inter-space compatibility are considered. To perform network optimization, the gradient of the kernel mapping is innovatively derived via structured matrix backpropagation in a theoretically principled way. Experiments on three challenging datasets show that our method achieves quite competitive performance compared with existing hashing methods.

Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails to exploit the target sequential information.Over-translation and under-translation errors often occur for the above reason, especially in the long sentence translation scenario.In this paper, we propose two approaches to retrieve the target sequential information for NAT to enhance its translation ability while preserving the fast-decoding property.Firstly, we propose a sequence-level training method based on a novel reinforcement algorithm for NAT (Reinforce-NAT) to reduce the variance and stabilize the training procedure.Secondly, we propose an innovative Transformer decoder named FS-decoder to fuse the target sequential information into the top layer of the decoder.Experimental results on three translation tasks show that the Reinforce-NAT surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed and the FS-decoder achieves comparable translation performance to the autoregressive Transformer with considerable speedup.

Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel network, called Spatio-Temporal Completion network (STCnet), to explicitly handle partial occlusion problem. Different from most previous works that discard the occluded frames, STCnet can recover the appearance of the occluded parts. For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame. For another, the temporal patterns of pedestrian sequence provide important clues to generate the contents of occluded parts. With the spatio-temporal information, STCnet can recover the appearance for the occluded parts, which could be leveraged with those unoccluded parts for more accurate video re-ID. By combining a re-ID network with STCnet, a video re-ID framework robust to partial occlusion (VRSTC) is proposed. Experiments on three challenging video re-ID databases demonstrate that the proposed approach outperforms the state-of-the-arts.

Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for small-scale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.

Weakly supervised learning is an essential problem in computer vision tasks, such as image classification, object recognition, etc., because it is expected to work in the scenarios where a large dataset with clean labels is not available. While there are a number of studies on weakly supervised image classification, they usually limited to either single-label or multi-label scenarios. In this work, we propose an effective approach for weakly supervised image classification utilizing massive noisy labeled data with only a small set of clean labels (e.g., 5%). The proposed approach consists of a clean net and a residual net, which aim to learn a mapping from feature space to clean label space and a residual mapping from feature space to the residual between clean labels and noisy labels, respectively, in a multi-task learning manner. Thus, the residual net works as a regularization term to improve the clean net training. We evaluate the proposed approach on two multi-label datasets (OpenImage and MS COCO2014) and a single-label dataset (Clothing1M). Experimental results show that the proposed approach outperforms the state-of-the-art methods, and generalizes well to both single-label and multi-label scenarios.

In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations. Inspired by the fact that facial actions are the movements of facial muscles, we depict the movements as the transformation between two face images in different frames and use it as the self-supervisory signal to learn the representations. However, under the uncontrolled condition, the transformation is caused by both facial actions and head motions. To remove the influence by head motions, we propose a Twin-Cycle Autoencoder (TCAE) that can disentangle the facial action related movements and the head motion related ones. Specifically, TCAE is trained to respectively change the facial actions and head poses of the source face to those of the target face. Our experiments validate TCAE's capability of decoupling the movements. Experimental results also demonstrate that the learned representation is discriminative for AU detection, where TCAE outperforms or is comparable with the state-of-the-art self-supervised learning methods and supervised AU detection methods.

Relationship is the core of scene graph, but its prediction is far from satisfying because of its complex visual diversity. To alleviate this problem, we treat relationship as an abstract object, exploring not only significative visual pattern but contextual information for it, which are two key aspects when considering object recognition. Our observation on current datasets reveals that there exists intimate association among relationships. Therefore, inspired by the successful application of context to object-oriented tasks, we especially construct context for relationships where all of them are gathered so that the recognition could benefit from their association. Moreover, accurate recognition needs discriminative visual pattern for object, and so does relationship. In order to discover effective pattern for relationship, traditional relationship feature extraction methods such as using union region or combination of subject-object feature pairs are replaced with our proposed intersection region which focuses on more essential parts. Therefore, we present our so-called Relationship Context - InterSeCtion Region (CISC) method. Experiments for scene graph generation on Visual Genome dataset and visual relationship prediction on VRD dataset indicate that both the relationship context and intersection region improve performances and realize anticipated functions.

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.

Combined variations such as low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face enhancement approaches only handle one type of variation per model, in this paper, we propose a deep generative adversarial network (FCSR-GAN) for joint face completion and face super-resolution via one model. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with partial occlusions. The discriminator of FCSR-GAN consists of two adversarial losses, a perceptual loss, and a face parsing loss, which assure the high quality of the recovered face images. Experimental results on several public-domain databases (CelebA and Helen) show that the proposed approach outperforms the state-of-the-art methods in jointly doing face super-resolution (up to 4×) and face completion from low-resolution face images with occlusions.

Face recognition plays an important role in computer vision. It still remains a challenging task due to pose, expression, illumination, partial occlusion, etc. In this work, we propose a novel Deformable Face Net (DFN) to handle the pose variations in face recognition. The Deformable Face Net introduces deformable convolution modules to simultaneously learn face recognition oriented alignment and feature extraction. Specifically, two loss functions, namely displacement consistency loss (DCL) and identity consistency loss (ICL) are designed to minimize the intra-class feature variation caused by different poses. These two loss functions jointly learn pose-aware displacement fields for deformable convolutions in the DFN. Different from the existing methods, the DFN focuses on aligning features across different poses rather than frontalizing the input faces. Extensive experiments show that the proposed DFN outperforms the state-of-the-art methods, especially on the datasets with large poses.

Future frame prediction in videos is promising avenue for unsupervised video representation learning. However video prediction has the huge solution space since the high-dimensionality and inherent uncertainty of the future video frames. Existing approaches impose weak constraints on the predictions, which results in motion confusion. To alleviate this problem, we propose a novel model named Bidirectional Constraint Network (BCnet). BCnet consists of forward prediction module and backward prediction module. The forward prediction module learns to predict the future sequence from the present sequence, while the backward prediction module learns to invert the task. The closed loop of the two modules allows that the backward prediction module generates informative feedback signals. The feedback signals clamp down the solution space of forward prediction module. Therefore, our approach can effectively alleviate the motion confusion. We further evaluate BCnet by fine-tuning it for a supervised learning problem: human action recognition on the UCF-101 dataset. We show that the representation help improve classification accuracy. Extensive experiments on several challenging public datasets show that our approach significantly outperforms state-of-the-art approaches, which demonstrates the effectiveness and generalization ability of our approach.

In this work, we propose an end-to-end approach for robust remote heart rate (HR) measurement gleaned from facial videos. Specifically the approach is based on remote photoplethysmography (rPPG), which constitutes a pulse triggered perceivable chromatic variation, sensed in RGB-face videos. Consequently, rPPGs can be affected in less-constrained settings. To unpin the shortcoming, the proposed algorithm utilizes a spatio-temporal attention mechanism, which places focus on the salient features included in rPPG-signals. In addition, we propose an effective rPPG augmentation approach, generating multiple rPPG signals with varying HRs from a single face video. Experimental results on the public datasets VIPL-HR and MMSE-HR show that the proposed method outperforms state-of-the-art algorithms in remote HR estimation.

Large-scale datasets have successively proven their fundamental importance in several research fields, especially for early progress in some emerging topics. In this paper, we focus on the problem of visual speech recognition, also known as lip-reading, which has received increasing interest in recent years. We present a naturally-distributed large-scale benchmark for lip-reading in the wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from more than 2,000 individual speakers. Each class corresponds to the syllables of a Mandarin word composed of one or several Chinese characters. To the best of our knowledge, it is currently the largest word-level lipreading dataset and also the only public large-scale Mandarin lip-reading dataset. This dataset aims at covering a "natural" variability over different speech modes and imaging conditions to incorporate challenges encountered in practical applications. It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several typical popular lip-reading methods and perform a thorough analysis of the results from several aspects. The results demonstrate the consistency and challenges of our dataset, which may open up some new promising directions for future work.

Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. In this paper, we propose a novel conditional CycleGAN for face sketch-to-photo transformation. Specifically, we leverage the advantages of CycleGAN and conditional GANs and design a feature-level loss to assure the high quality of the generated face photos from sketches. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of mugshot photos. Experimental results on the public-domain database CUFSF show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large gallery set.

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

Generally, we human follow the roughly common aging trends, e.g., the wrinkles only tend to be more, longer or deeper. However, the aging process of each individual is more dominated by his/her personalized factors, including the invariant factors such as identity and mole, as well as the personalized aging patterns, e.g., one may age by graying hair while another may age by receding hairline. Following this biological principle, in this work, we propose an effective and efficient method to simulate natural aging. Specifically, a personalized aging basis is established for each individual to depict his/her own aging factors. Then different ages share this basis, being derived through age-specific transforms. The age-specific transforms represent the aging trends which are shared among all individuals. The proposed method can achieve continuous face aging with favorable aging accuracy, identity preservation, and fidelity. Furthermore, befitted from the effective design, a unique model is capable of all ages and the prediction time is significantly saved.

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive Network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.

Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 53.7% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.

Emotion recognition and facial action unit(AU) detection are the most two prevalent tasks in facial expression analysis. Since the two tasks are highly correlated, in this paper, we simultaneously do emotion recognition and AU detection in a multi-task learning framework to make the tasks benefit from each other. To achieve this, we propose an Adaptively Weights Sharing Network (AWS-Net) that automatically learns where and to what extent each task should borrow information from the other by placing an AWS-Unit after each layer-pair of the two tasks' networks. The proposed AWS-Net is end-to-end trainable on data that is merely annotated with emotions or AUs. Experimental results on several facial expression recognition(FER) datasets demonstrate that AWS-Net improves the performance of both single-task models(emotion recognition and AU detection) and it outperforms other state-of-the-art multi-task learning strategies in FER.

When applying common object detection algorithms to detect small objects on high-resolution images, the down-sampling operation of the input images is inevitable due to the limitation of GPU memory. Accordingly, the details for characterizing small objects are lost. To resolve this contradiction, a small object detection method in a coarse-to-fine manner is presented. Specifically, some rough regions of interest (ROI) are firstly computed from low-resolution images. The prior knowledge of the positions of objects is used to guide the generation of ROIs. Then the features of small ROIs are recomputed from high-resolution images, and the features of large ROIs are obtained from the feature maps used to generate ROIs. The proposed method is validated on two datasets. One is a plant phenotyping dataset and the other is a public traffic sign dataset. Experimental results convincingly show the effectiveness of the proposed method.

Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples. The unseen classes and low-data problem make few-shot classification very challenging. Many existing approaches extracted features from labeled and unlabeled samples independently, as a result, the features are not discriminative enough. In this work, we propose a novel Cross Attention Network to address the challenging problems in few-shot classification. Firstly, Cross Attention Module is introduced to deal with the problem of unseen classes. The module generates cross attention maps for each pair of class feature and query sample feature so as to highlight the target object regions, making the extracted feature more discriminative. Secondly, a transductive inference algorithm is proposed to alleviate the low-data problem, which iteratively utilizes the unlabeled query set to augment the support set, thereby making the class features more representative. Extensive experiments on two benchmarks show our method is a simple, effective and computationally efficient framework and outperforms the state-of-the-arts.

Facial action units (AUs) recognition is essential for emotion analysis and has been widely applied in mental state analysis. Existing work on AU recognition usually requires big face dataset with accurate AU labels. However, manual AU annotation requires expertise and can be time-consuming. In this work, we propose a semi-supervised approach for AU recognition utilizing a large number of web face images without AU labels and a small face dataset with AU labels inspired by the co-training methods. Unlike traditional co-training methods that require provided multi-view features and model re-training, we propose a novel co-training method, namely multi-label co-regularization, for semi-supervised facial AU recognition. Two deep neural networks are used to generate multi-view features for both labeled and unlabeled face images, and a multi-view loss is designed to enforce the generated features from the two views to be conditionally independent representations. In order to obtain consistent predictions from the two views, we further design a multi-label co-regularization loss aiming to minimize the distance between the predicted AU probability distributions of the two views. In addition, prior knowledge of the relationship between individual AUs is embedded through a graph convolutional network (GCN) for exploiting useful information from the big unlabeled dataset. Experiments on several benchmarks show that the proposed approach can effectively leverage large datasets of unlabeled face images to improve the AU recognition robustness and outperform the state-of-the-art semi-supervised AU recognition methods.

The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.

Attributes are mid-level semantic properties of objects. Recent research has shown that visual attributes can benefit many typical learning problems in computer vision community. However, attribute learning is still a challenging problem as the attributes may not always be predictable directly from input images and the variation of visual attributes is sometimes large across categories. In this paper, we propose a unified multiplicative framework for attribute learning, which tackles the key problems. Specifically, images and category information are jointly projected into a shared feature space, where the latent factors are disentangled and multiplied to fulfil attribute prediction. The resulting attribute classifier is category-specific instead of being shared by all categories. Moreover, our model can leverage auxiliary data to enhance the predictive ability of attribute classifiers, which can reduce the effort of instance-level attribute annotation to some extent. By integrated into an existing deep learning framework, our model can both accurately predict attributes and learn efficient image representations. Experimental results show that our method achieves superior performance on both instance-level and category-level attribute prediction. For zero-shot learning based on visual attributes and human-object interaction recognition, our method can improve the state-of-the-art performance on several widely used datasets.

In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 ×6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.

Zero-shot learning (ZSL) has enjoyed great popularity in recent years due to its ability to recognize novel objects, where semantic information is exploited to build up relations among different categories. Traditional ZSL approaches usually focus on learning more robust visual-semantic embeddings among seen classes and directly apply them to the unseen classes without considering whether they are suitable. It is well known that domain gap exists between seen and unseen classes. In order to tackle such problem, we propose a novel adaptive metric learning approach to measure the compatibility between visual samples and class semantics, where class similarities are utilized to adapt the visual-semantic embedding to the unseen classes. Extensive experiments on four benchmark ZSL datasets show the effectiveness of the proposed approach.

Facial attribute editing aims to manipulate single or multiple attributes on a given face image, i.e., to generate a new face image with desired attributes while preserving other details. Recently, the generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results. Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of a given face conditioned on the desired attributes. Some existing methods attempt to establish an attribute-independent latent representation for further attribute editing. However, such attribute-independent constraint on the latent representation is excessive because it restricts the capacity of the latent representation and may result in information loss, leading to over-smooth or distorted generation. Instead of imposing constraints on the latent representation, in this work, we propose to apply an attribute classification constraint to the generated image to just guarantee the correct change of desired attributes, i.e., to change what you want. Meanwhile, the reconstruction learning is introduced to preserve attribute-excluding details, in other words, to only change what you want. Besides, the adversarial learning is employed for visually realistic editing. These three components cooperate with each other forming an effective framework for high quality facial attribute editing, referred as AttGAN. Furthermore, the proposed method is extended for attribute style manipulation in an unsupervised manner. Experiments on two wild datasets, CelebA and LFW, show that the proposed method outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved.

Facial expression recognition in the wild is challenging due to various un-constrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces that are common in the wild. In this paper, we propose a Convolution Neutral Network with attention mechanism (ACNN) that can perceive the occlusion regions of the face and focus on the most discriminative unoccluded regions. ACNN is an end to end learning framework. It combines the multiple representations from facial regions of interest (ROIs). Each representation is weighed via a proposed Gate Unit that computes an adaptive weight from the region itself according to the unobstructed-ness and importance. Considering different RoIs, we introduce two versions of ACNN: patch based ACNN (pACNN) and global-local based ACNN (gACNN). pACNN only pays attention to local facial patches. gACNN integrates local representations at patch-level with global representation at image-level. The proposed ACNNs are evaluated on both real and synthetic occlusions, including a self-collected facial expression dataset with real-world occlusions (FED-RO), two largest in-the-wild facial expression datasets (RAF-DB and AffectNet) and their modifications with synthesized facial occlusions. Experimental results show that ACNNs improve the recognition accuracy on both the non-occluded faces and occluded faces. Visualization results demonstrate that, compared with the CNN without Gate Unit, ACNNs are capable of shifting the attention from the occluded patches to other related but unobstructed ones. ACNNs also outperform other state-of-the-art methods on several widely used in-the-lab facial expression datasets under the cross-dataset evaluation protocol.

Research in texture recognition often concentrates on recognizing textures with intraclass variations, such as illumination, rotation, viewpoint, and small-scale changes. In contrast, in real-world applications, a change in scale can have a dramatic impact on texture appearance to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are among the hardest to handle. In this paper, we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a genetic algorithm to change the filters in the hidden layers during network training in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a Fisher vector pooling of a convolutional neural network filter bank feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding, we are developing a new dataset, the extreme scale variation textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms the gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing the superior performance compared with the state-of-the-art.

Visual sign language recognition is an interesting and challenging problem. To create a discriminative representation, a hierarchical Grassmann covariance matrix (HGCM) model is proposed for sign description. Furthermore, a multi-temporal belief propagation (MTBP) based segmentation approach is presented for continuous sequence spotting. Concretely speaking, a sign is represented by multiple covariance matrices, followed by evaluating and selecting their most significant singular vectors. These covariance matrices are transformed into a more compact and discriminative HGCM, which is formulated on the Grassmann manifold. Continuous sign sequences can be recognized frame by frame using the HGCM model, before being optimized by MTBP, which is a carefully designed graphic model. The proposed method is thoroughly evaluated on isolated and synthetic and real continuous sign datasets as well as on HDM05. Extensive experimental results convincingly show the effectiveness of our proposed framework.

Visual relationship detection aims to describe the interactions between pairs of objects. Different from individual object learning tasks, the number of possible relationships are much larger, which makes it hard to explore only based on the visual appearance of objects. In addition, due to the limited human effort, the annotations for visual relationships are usually incomplete which increases the difficulty of model training and evaluation. In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection. To complement the representation ability of visual appearance, we integrate multiple cues for predicting the relationships contained in an input image. Moreover, we design a new ranking objective function by enforcing the annotated relationships to have higher relevance scores. Unlike previous works, our proposed method can both facilitate the co-occurrence of relationships and mitigate the incompleteness problem. Experimental results show that our proposed method outperforms the state-of-the-art on the two widely used datasets. We also demonstrate its superiority in detecting zero-shot relationships.

Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.

Context is important for accurate visual recognition. In this work we propose an object detection algorithm that not only considers object visual appearance, but also makes use of two kinds of context including scene contextual information and object relationships within a single image. Therefore, object detection is regarded as both a cognition problem and a reasoning problem when leveraging these structured information. Specifically, this paper formulates object detection as a problem of graph structure inference, where given an image the objects are treated as nodes in a graph and relationships between the objects are modeled as edges in such graph. To this end, we present a so-called Structure Inference Network (SIN), a detector that incorporates into a typical detection framework (e.g. Faster R-CNN) with a graphical model which aims to infer object state. Comprehensive experiments on PASCAL VOC and MS COCO datasets indicate that scene context and object relationships truly improve the performance of object detection with more desirable and reasonable outputs.

Age estimation has wide applications in video surveillance, social networking, and human-computer interaction. Many of the published approaches simply treat age estimation as an exact age regression problem, and thus do not leverage a distribution's robustness in representing labels with ambiguity such as ages. In this paper, we propose a new loss function, called mean-variance loss, for robust age estimation via distribution learning. Specifically, the mean-variance loss consists of a mean loss, which penalizes difference between the mean of the estimated age distribution and the ground-truth age, and a variance loss, which penalizes the variance of the estimated age distribution to ensure a concentrated distribution. The proposed mean-variance loss and softmax loss are jointly embedded into Convolutional Neural Networks (CNNs) for age estimation. Experimental results on the FG-NET, MORPH Album II, CLAP2016, and AADB databases show that the proposed approach outperforms the state-of-the-art age estimation methods by a large margin, and generalizes well to image aesthetics assessment.

Rotation-invariant face detection, i.e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances. Most existing methods compromise with speed or accuracy to handle the large RIP variations. To address this problem more efficiently, we propose Progressive Calibration Networks (PCN) to perform rotation-invariant face detection in a coarse-to-fine manner. PCN consists of three stages, each of which not only distinguishes the faces from non-faces, but also calibrates the RIP orientation of each face candidate to upright progressively. By dividing the calibration process into several progressive steps and only predicting coarse orientations in early stages, PCN can achieve precise and fast calibration. By performing binary classification of face vs. non-face with gradually decreasing RIP ranges, PCN can accurately detect faces with full 360° RIP angles. Such designs lead to a real-time rotation-invariant face detector. The experiments on multi-oriented FDDB and a challenging subset of WIDER FACE containing rotated faces in the wild show that our PCN achieves quite promising performance.

Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias.Sequence-level training under the reinforcement framework can mitigate the problems of the word-level loss, but its performance is unstable due to the high variance of the gradient estimation.On these grounds, we present a method with a differentiable sequence-level training objective based on probabilistic n-gram matching which can avoid the reinforcement framework.In addition, this method performs greedy search in the training which uses the predicted words as context just as at inference to alleviate the problem of exposure bias.Experiment results on the NIST Chinese-to-English translation tasks show that our method significantly outperforms the reinforcement-based algorithms and achieves an improvement of 1.5 BLEU points on average over a strong baseline system.

RGB-D face recognition has attracted increasing attentions in recent years because of its robustness in unconstrained environment. However, existing approaches either handle individual modalities using completely separate pipelines or treat all the modalities equally using the same pipeline. Such approaches did not adequately consider the modality differences and exploit the modality correlations. We propose a novel approach for RGB-D face recognition that is able to learn complementary features from multiple modalities and common features between different modalities. Specifically, we introduce a joint loss taking activation from both modality-specific feature learning networks, and enforcing the features to be learned in a complementary way. We further extend the capability of this multi-modality (e.g., RGB-D vs. RGB-D) matcher into cross-modality (e.g., RGB vs. RGB-D) scenarios by learning a common feature transformation mapping different modalities into the same feature space. Experimental results on a number of public RGB-D face databases (e.g., EURECOM, VAP, IIIT-D, and BUAA), and a large RGB-D database we collected, show the impressive performance of the proposed approach.

Pedestrian head with arbitrary poses and size is prohibitively difficult to detect in many real world applications. An appealing alternative is to utilize object detection technologies, which tend to be more and more mature and faster. However, general object detection technologies can hardly work in complicated scenarios where many heads are often too small to detect. In this paper, we present a novel approach that learns a semantic connection between pedestrian head and other body parts for head detection. Specifically, the proposed model, named as HeadNet, is based on PVANet backbone and also introduces beneficial strategies including online hard example mining (OHEM), fine-grained feature maps, RoI Align and Body in Context (BiC). Experiments demonstrate that our approach is able to utilize spatial semantics of the entire body effectively, and gains inspiring performance for pedestrian head detection.

Hand pose estimation is an attractive problem in computer vision for its key role in gesture controlled humancomputer interaction (HCI) applications. This problem focuses on revealing the hand skeleton structure from visual information. However, it is very challenging for complicated hand configurations. In this paper, a kinematic constrained cascaded autoencoder regression (KCAE) framework is proposed to estimate the hand pose from a single depth image.We introduce a two-stage cascaded structure to regress the palm direction and the whole hand joints successively. In addition, the edge constraints are first introduced to the loss function with an endto- end manner, which maintains the kinematics of hands and makes the prediction more reasonable. With this framework, different features are evaluated, including the handcrafted features and the features learned from CNN. The experiments widely conducted on our collected dataset and the public MSRA hand gesture database demonstrate the effectiveness of KCAE. Overall, The proposed method achieves comparable performance with state-of-the-arts.

Face alignment plays an important role for robust face recognition and analysis applications in the wild. While a number of face alignment methods are available, large-pose face alignment remains a very challenging problem due to the ambiguity of facial keypoints in 2D face images. Recent attempts to solve this problem via 3D model fitting show more robustness against large poses and 2D ambiguity, but their accuracy and speed are still limited. We propose a 3D reconstruction based method to quickly and accurately detect 2D facial landmarks and estimate their visibilities. By designing a cascaded multi-task CNN model, we can efficiently reconstruct the 3D face shape, together with pose estimation as an auxiliary task. Finally, the landmarks on 3D shape are projected to the 2D face image to get the 2D landmarks and their visibilities. Experimental results on the challenging 300W-LP, AFLW2000-3D, and AFLW databases show that the proposed approach can be comparable with the state-of-the-art methods and is able to run in real time (32ms per image) on 3.4 GHz CPU.

Recent progress of face recognition benefits a lot from large-scale face datasets with deep Convoluitonal Neural Networks(CNN). However, when dataset contains a large number of subjects but with few samples for each subject, conventional CNN with softmax loss is heavily prone to overfitting. To address this issue, we propose a hierarchical training schema to optimize CNN with coarse-to-fine class labels, referred to as Hit-CNN. Firstly trained with coarse class labels and then refined with fine class labels, Hit-CNN is enabled the to capture the distribution of data from major variations to fine variations progressively, which can effectively relieve the overfitting and lead to better generalization. In this work, the hierarchical coarse-to-fine class labels are obtained via hierarchical k-means clustering according to the face identities. Evaluated on two face datasets, the proposed Hit-CNN provides better results compared with the conventional CNN under the circumstances of large-scale data with few samples per subject.

This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking. Existing state-of-the-art tracking methods do not deal with temporal relationship in video sequences, which leads to imperfect feature representations. To address this problem, CD-CNN models temporal appearance continuity based on the idea of temporal slowness. Mathematically, we prove that, by introducing temporal appearance continuity into tracking, the upper bound of target appearance representation error can be sufficiently small with high probability. Further, in order to alleviate inaccurate target localization and drifting, we propose a novel notion, object-centroid, to characterize not only objectness but also the relative position of the target within a given patch. Both temporal appearance continuity and object-centroid are jointly learned during offline training and then transferred for online tracking. We evaluate our tracker through extensive experiments on two challenging benchmarks and show its competitive tracking performance compared with state-of-the-art trackers.

In this paper, we propose an automatic engagement prediction method for the Engagement in the Wild sub-challenge of EmotiW 2018. We first design a novel Gaze-AU-Pose (GAP) feature taking into account the information of gaze, action units and head pose of a subject. The GAP feature is then used for the subsequent engagement level prediction. To efficiently predict the engagement level for a long-time video, we divide the long-time video into multiple overlapped video clips and extract GAP feature for each clip. A deep model consisting of a Gated Recurrent Unit (GRU) layer and a fully connected layer is used as the engagement predictor. Finally, a mean pooling layer is applied to the per-clip estimation to get the final engagement level of the whole video. Experimental results on the validation set and test set show the effectiveness of the proposed approach. In particular, our approach achieves a promising result with an MSE of 0.0724 on the test set of Engagement Prediction Challenge of EmotiW 2018.t with an MSE of 0.072391 on the test set of Engagement Prediction Challenge of EmotiW 2018.

This paper introduces a new benchmark for 360-degree rotation head tracking, named Full Rotation Head Tracking (FRHT). The benchmark consists of 50 color sequences containing diverse human activities with complicated head motions. Specially, FRHT covers the most challenges of head tracking and focuses on the appearance variations of heads during the 360-degree rotation. It also pays attention to the clutters from the heads of nearby people. Further, we propose a baseline tracker. It guides a selective adaption updating by verifying strategies, thus alleviates error accumulation. Extensive experiments validate the advantages of FRHT in head rotation and similar object clutter.

Facial expression recognition in the wild is challenging due to various un-constrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces that are common in the wild. In this paper, we propose an end-to-end trainable Patch-Gated Convolution Neutral Network (PG-CNN) that can automatically percept the occluded region of the face and focus on the most discriminative un-occluded regions. To determine the possible regions of interest on the face, PG-CNN decomposes an intermediate feature map into several patches according to the positions of related facial landmarks. Then, via a proposed Patch-Gated Unit, PG-CNN reweighs each patch by the unobstructed-ness or importance that is computed from the patch itself. The proposed PG-CNN is evaluated on two largest in-the-wild facial expression datasets (RAF-DB and AffectNet) and their modifications with synthesized facial occlusions. Experimental results show that PG-CNN improves the recognition accuracy on both the original faces and faces with synthesized occlusions. Visualization results demonstrate that, compared with the CNN without Patch-Gated Unit, PG-CNN is capable of shifting the attention from the occluded patch to other related but unobstructed ones. Experiments also show that PG-CNN outperforms other state-of-the-art methods on several widely used in-the-lab facial expression datasets under the cross-dataset evaluation protocol.

Face anti-spoofing has encountered increasing demand as one of the key technologies for reliable and safe authentication with faces. Current face anti-spoofing methods generally take a single crop of face region as input for classification, i.e. exploiting information at only one scale. This single-scale scheme mainly focuses on facial characteristics but not utilize the surrounding information, causing poor generalization for different scenarios with varied means of attacks. Besides, it is tedious or highly empirical to determine an optimal scale of face crops. To overcome the limitations of single-scale methods, in this work we propose to integrate Multi-Scale information for better Face ANti-Spoofing (MS-FANS). Specifically, the proposed MS-FANS method takes multiple face crops at different scales as input followed by a convolutional neural network (CNN) for feature extraction. Then the features from different scales form as a sequence, which are fed into a Long Short-Term Memory (LSTM) network for adaptive fusion of multi-scale information, constructing the final representation for classification. Benefited from this multi-scale design, MS-FANS can adaptively utilize context information from multiple scales, leading to promising performance on two challenging face anti-spoofing datasets, Idiap REPLAY-ATTACK and CASIA-FASD, with significant improvement compared with the existing methods.

Remote photoplethysmography (rPPG) based noncontact heart rate (HR) measurement from a face video has drawn increasing attention recently because of its potential applications in many scenarios such as training aid, health monitoring, and nursing care. Although a number of methods have been proposed, most of them are designed under certain assumptions and could fail when such assumptions do not hold. At the same time, while deep learning based methods have been reported to achieve promising results in many computer vision tasks, their use in rPPG-based heart rate estimation has been limited due to the very limited data available in public domain. To overcome this limitation and leverage the strong modeling ability of deep neural networks, in this paper, we propose a novel spatial-temporal representation for the HR signal and design a general-to-specific transfer learning strategy to train a deep heart rate estimator from a large volume of synthetic rhythm signals and a limited number of available face video data. Experiment results on the public-domain databases show the effectiveness of the proposed approach.

Age estimation has broad applications in many fields, such as video surveillance, social networking, and human-computer interaction. Many of the existing approaches treat age estimation as a classification problem; however, the individual age values are not independent classes; they have an ordinal relationship. Classification loss such as softmax is not able to model such kind of relationship. In this paper, we propose a new loss, called revised contrastive loss, to model the ordinal relationship of individual ages. Specifically, the revised contrastive loss is proposed to penalize the distance between two face images in the feature space according to their age difference, which makes the learned features more discriminative for the age estimation task. We embed the proposed revised contrastive loss and softmax loss into a Convolutional Neural Network (CNN), and optimize the networks via Stochastic Gradient Descent (SGD) in an end-to-end fashion. Experimental results on a number of challenging face aging databases (FG-NET, MORPH Album II, and CLAP2016) show that the proposed approach outperforms the state-of-the-art methods by a large margin using a single model.

Despite tremendous progress in scene text detection in the past few years, efficient text detection in the wild remains challenging, particularly for the texts have large rotations, and the complicated background areas that are easily confused with text. In this paper, we propose an effective approach for scene text detection, which consists of initial text detection using the proposed deep semantic feature fusion of a fully convolutional network (FCN), and text detection refinement by our attention based text vs. non-text classifier learned in a fine-to-coarse fashion. The proposed approach outperforms the state-of-the-art scene text detection algorithms on the public-domain ICDAR2015 dataset, achieving an accuracy of 0.83 in terms of F-measure.

Abstract Headsets for virtual reality such as head‐mounted displays have become ubiquitous and bring immersive experiences to individual users. People who stand outside the virtual world may want to share the same scenes that are shown on the screen of the headset. It is therefore of great importance to merge real and virtual worlds into the same environment, where physical and virtual objects exist simultaneously and interact in real time. We propose shared augmented virtual environment (SAVE), a mixed reality (MR) system that overlays the virtual world with real objects captured by a Kinect depth camera. We refine the depth map and exploit a Graphics Processing Unit (GPU) based natural image matting method to obtain the real objects from cluttered scenes. In the synthetic MR world, we can render real and virtual objects in real time and handle the depth from both worlds properly. The advantage of our system is that we connect the virtual and real worlds with a bridge controller mounted on the Kinect and need to calibrate the whole system only once before use. Our results demonstrate that the proposed SAVE system is able to create high‐quality 1080p live MR footage, enabling realistic virtual experiences to be shared among a number of people in potential applications such as education, design, and entertainment.

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal versus nominal and holistic versus local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geometry of the underlying Riemannian manifolds. In this paper, we propose a novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse average appearance and pattern variation of faces within one video. The proposed metric learning framework can handle three typical tasks of video-based face recognition: Video-to-Still, Still-to-Video and Video-to-Video settings. To accomplish this new framework, by exploiting typical Riemannian geometries for kernel embedding, we map the source Euclidean space and Riemannian manifold into a common Euclidean subspace, each through a corresponding high-dimensional Reproducing Kernel Hilbert Space (RKHS). With this mapping, the problem of learning a cross-view metric between the two source heterogeneous spaces can be converted to learning a single-view Euclidean distance metric in the target common Euclidean space. By learning information on heterogeneous data with the shared label, the discriminant metric in the common space improves face recognition from videos. Extensive experiments on four challenging video face databases demonstrate that the proposed framework has a clear advantage over the state-of-the-art methods in the three classical video-based face recognition scenarios.

Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, which has a well-established Riemannian structure. Under this solution, we exploit a new supervised SPDSL technique to learn the manifold-manifold transformation by regressing the similarities of selected SPD data pairs to their ground-truth similarities on the target SPD manifold. To optimize the proposed objective function, we further derive an optimization algorithm on the PSD manifold. Evaluations on three visual classification tasks show the advantages of the proposed approach over the existing SPD-based discriminant learning methods.

To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.

In this paper we propose a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes. Considering the scale of modern datasets, hashing is favorable for its low complexity. However, most existing hashing methods are designed to preserve one single kind of similarity, thus incapable of dealing with the different tasks simultaneously. To overcome this limitation, we propose a new hashing method, named Dual Purpose Hashing (DPH), which jointly preserves the category and attribute similarities by exploiting the convolutional networks (CNN) to hierarchically capture the correlations between category and attributes. Since images with both category and attribute labels are scarce, our method is designed to take the abundant partially labelled images on the Internet as training inputs. With such a framework, the binary codes of new-coming images can be readily obtained by quantizing the network outputs of a binary-like layer, and the attributes can be recovered from the codes easily. Experiments on two large-scale datasets show that our dual purpose hash codes can achieve comparable or even better performance than those state-of-the-art methods specifically designed for each individual retrieval task, while being more compact than the compared methods.

For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. The framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. The proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.

Facial landmark detection plays an important role in computer vision. It is a challenging problem due to various poses, exaggerated expressions and partial occlusions. In this work, we propose a Fully End-to-End Cascaded Convolutional Neural Network (FEC-CNN) for more promising facial landmark detection. Specifically, FEC-CNN includes several sub- CNNs, which progressively refine the shape prediction via finer and finer modeling, and the overall network is optimized fully end-to-end. Experiments on three challenging datasets, IBUG, 300W competition and AFLW, demonstrate that the proposed method is robust to large poses, exaggerated expressions and partial occlusions. The proposed FEC-CNN significantly improves the accuracy of landmark prediction.

Face recognition is an important problem in computer vision, however, it is still challenging due to a few wild factors, such as large variations caused by pose, expression, lighting, etc. In this work, we mainly focus on dealing with the pose variations for face recognition. The proposed method attempts to directly transform a non-frontal face image into frontal one by Learning a Displacement Field network (LDFNet) and then recognizes with the transformed images. The existing methods, that follow the same scheme of transforming non-frontal faces into frontal ones, either transform by using 3D-model (3D methods) or transform by using 2D reconstructive methods (2D methods). The 3D methods may lead to the invisibility of some pixels in the transformed frontal images, while the 2D methods may lead to difference between the pixels in the transformed frontal images and the original non-frontal images. Our proposed LDF-Net method can handle these two problems by learning a morphable displacement field for each pixel in the transformed frontal image. Therefore, LDF-Net can achieve a frontal image where all pixels are from the original non-frontal image pixels and no invisible pixels exist, so as to maintain the informative information from the non-frontal images as much as possible. The experiments on MultiPIE dataset show that the proposed LDF-Net achieves state-of-theart performance for face recognition across pose, especially for those large poses.

Large-scale face data together with deep learningtechnology have significantly improved the performance of facerecognition in the wild. Hereinto, the large-scale face data playsa fundamental role, and it is nontrivial to collect a large-scaleface dataset with accurate class labels. No wonder it is quitemoney and effort consuming by collecting manually, howeverit is easy to access large scale face images by using a searchengine with names as keywords. Unfortunately, the retrievedface images from search engine are usually messed up withsome noise images with wrong labels, which forms a greatneed of developing algorithms to refine the retrieved noisyface image set. In this work, we propose a joint frameworkin which multiple noisy face image sets refining collaborateswith the discriminant feature space learning. Specifically, thetwo modules, refining each noisy face image set by conductingone-class classification based on learnt discriminant feature andlearning discriminant feature space based on refined face imagesets, are updated iteratively inducing an effective refinementmodel. To investigate the proposed method, we collect a realworlddataset for the evaluation including 15,515 images of46 subjects with 40% ~ 63.5% noise images per subject. Theexperimental results demonstrate that state-of-the-art one-classclassification methods can be significantly improved whenbeing embedded in the proposed framework, and the proposedframework exhibits strong robustness even when the mean noiseproportion is up to 50% ~ 80%.

Convolutional Neural Network (CNN) together with large-scale labeled data has achieved the state-of-the-art accuracy in various computer vision tasks. In real-world settings, however, the labels of large scale data can be noisy, which shall seriously degenerate the performance of CNN. In this work, we propose a self-error-correcting CNN (SECCNN) to deal with the noisy labels problem, by simultaneously correcting the improbable labels and optimizing the deep model. Specifically, the SEC-CNN provides an opportunity to correct a wrong label by developing a confidence policy to switch between the label of the sample and the max-activated output neuron of the CNN. Based on the assumption that the deep model is more and more accurate during the training, the confidence policy relies more on the given labels at the beginning stages, but tends to believe that the max-activated neuron of the learned network is reliable. SEC-CNN enables CNN learning to be effective even with 80% noisy labels. Extensive experimental results on MNIST, CIFAR-10, ImageNet and CCFD face dataset demonstrate the effectiveness of the proposed method in dealing with noisy labels.

Face attribute prediction has important applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute prediction, most of them did not explicitly consider the attribute correlation and heterogeneity during feature learning. In this paper, we propose a Deep Multi-Task Learning (DMTL) network to jointly learn multiple models; each addresses the prediction of one category of homogenous attributes. Specifically, we group the heterogeneous face attributes into two categories (i.e., nominal and ordinal), and design corresponding prediction models. At the same time, we use a convolutional neural network (CNN) for early stage feature learning, which is shared by all the attributes. Experiments on the public-domain MORPH II, CelebA, and LFWA databases show that the proposed approach outperforms the state of the art in joint face attribute prediction, and has good generalization ability.

Non-contact heart rate (HR) measurement via remote photoplethysmography (rPPG) has drawn increasing attention. While a number of methods have been reported, most of them did not take into account the continuous HR measurement problem, which is more challenging due to limited observed video frames and the requirement of speed. In this paper, we present a real-time rPPG method for continuous HR measurement from face videos. We use a multi-patch ROI strategy to remove outlier signals. Chrominance feature is then generated from each ROI to reduce the color channel magnitude differences, which is followed by temporal filtering to suppress the artifacts. In addition, considering the temporal relationship of neighboring HR rhythms, we learn a HR distribution based on historical HR measurements, and apply it to the succeeding HR estimations. Experiment results on the public-domain MAHNOB-HCI database and user tests with commodity webcams show the effectiveness of the proposed approach.

Convolutional Neural Network (CNN) has led to significant progress in face recognition. Currently most CNN-based face recognition methods follow a two-step pipeline, i.e. a detected face is first aligned to a canonical one predefined by a mean face shape, and then it is fed into a CNN to extract features for recognition. The alignment step transforms all faces to the same shape, which can cause loss of geometrical information which is helpful in distinguishing different subjects. Moreover, it is hard to define a single optimal shape for the following recognition, since faces have large diversity in facial features, e.g. poses, illumination, etc. To be free from the above problems with an independent alignment step, we introduce a Recursive Spatial Transformer (ReST) module into CNN, allowing face alignment to be jointly learned with face recognition in an end-to-end fashion. The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations. To model non-rigid transformation, multiple ReST modules are organized in a hierarchical structure to account for different parts of faces. Overall, the proposed ReST can handle large face variations and non-rigid transformation, and is end-to-end learnable and adaptive to input, making it an effective alignment-free face recognition solution. Extensive experiments are performed on LFW and YTF datasets, and the proposed ReST outperforms those two-step methods, demonstrating its effectiveness.

The complete plenoptic function records radiance of rays from every location, at every angle, for every wavelength and at every time. The signal is multi-dimensional and has long relied on multi-modal sensing such as hybrid light field camera arrays. In this paper, we present a single camera hyperspectral light field imaging solution that we call Snapshot Plenoptic Imager (SPI). SPI uses spectral coded catadioptric mirror arrays for simultaneously acquiring the spatial, angular and spectral dimensions. We further apply a learning-based approach to improve the spectral resolution from very few measurements. Specifically, we demonstrate and then employ a new spectral sparsity prior that allows the hyperspectral profiles to be sparsely represented under a pre-trained dictionary. Comprehensive experiments on synthetic and real data show that our technique is effective, reliable, and accurate. In particular, we are able to produce the first wide FoV multi-spectral light field database.

Treating visual tracking as a matching problem, siamese architecture has drawn increasing interest recently. In this paper, we propose a novel siamese recurrent architecture that can enhance the similarity matching by leveraging contextual information. Specifically, the multi-directional Recurrent Neural Network (RNN) is employed to memorize the long-range contextual dependencies of object parts and learn the self-structure information of the object. We test the proposed method on a challenging benchmark, and it gain promising results compared with the existing tracking algorithms.

Visual attribute learning is a fundamental and challenging problem for image understanding. Considering the huge semantic space of attributes, it is economically impossible to annotate all their presence or absence for a natural image via crowd-sourcing. In this paper, we tackle the incompleteness nature of visual attributes by introducing auxiliary labels into a novel transductive learning framework. By jointly predicting the attributes from the input images and modeling the relationship of attributes and auxiliary labels, the missing attributes can be recovered effectively. In addition, the proposed model can be solved efficiently in an alternative way by optimizing quadratic programming problems and updating parameters in closed-form solutions. Moreover, we propose and investigate different methods for acquiring auxiliary labels. We conduct experiments on three widely used attribute prediction datasets. The experimental results show that our proposed method can achieve the state-of-the-art performance with access to partially observed attribute annotations.

Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence (AI) systems. For the next-generation AI, called ‘AI 2.0’, one of the most significant features will be that AI is empowered with intelligent perceptual capabilities, which can simulate human brain’s mechanisms and are likely to surpass human brain in terms of performance. In this paper, we briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines. On this basis, we envision several R&D trends in intelligent perception for the forthcoming era of AI 2.0, including: (1) human-like and transhuman active vision; (2) auditory perception and computation in an actual auditory setting; (3) speech perception and computation in a natural interaction setting; (4) autonomous learning of perceptual information; (5) large-scale perceptual information processing and learning platforms; and (6) urban omnidirectional intelligent perception and reasoning engines. We believe these research directions should be highlighted in the future plans for AI 2.0.

This letter presents a prototype discriminative learning (PDL) method for image set classification. We aim to simultaneously learn prototypes and a linear discriminative projection to drive that in the target subspace each image set can be discriminated with its nearest neighbor prototype. To reveal the unseen appearance variations implicitly in an image set, the prototypes are actually “virtual,” which do not certainly appear in the set but are searched in the corresponding affine hull. Moreover, to enhance the stability and robustness of the learned target subspace, an orthogonality constraint is imposed on the projection. Thus, to optimize the prototypes and the projection jointly, we design a specific gradient descent mechanism by updating the projection on Stiefel manifold and the prototypes in Euclidean space in an alternative optimization manner. Experimental results on four challenging databases demonstrate the superiority of the proposed PDL method.

Cross-view recognition that intends to classify samples between different views is an important problem in computer vision. The large discrepancy between different even heterogenous views make this problem quite challenging. To eliminate the complex (maybe even highly nonlinear) view discrepancy for favorable cross-view recognition, we propose a multi-view deep network (MvDN), which seeks for a non-linear discriminant and view-invariant representation shared between multiple views. Specifically, our proposed MvDN network consists of two sub-networks, view-specific sub-network attempting to remove view-specific variations and the following common sub-network attempting to obtain common representation shared by all views. As the objective of MvDN network, the Fisher loss, i.e. the Rayleigh quotient objective, is calculated from the samples of all views so as to guide the learning of the whole network. As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative. The experiments of face recognition across pose and face recognition across feature type on three datasets with 13 and 2 views respectively demonstrate the superiority of the proposed method, especially compared to the typical linear ones.

In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.

Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.

The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.

In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based on the accurate hand positions provided by the hand detector trained from Faster R-CNN. While in the recognition module, 2S-RNN is designed to efficiently fuse multi-modal features, i.e. the RGB and depth channels. The experimental results on both the validation and test sets of the Continuous Gesture Dataset (ConGD) have shown promising performance of the proposed framework. We ranked 1st in the ChaLearn LAP Large-scale Continuous Gesture Recognition Challenge with the mean Jaccard Index of 0.286915.

In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple linear transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.

We address the problem of face video retrieval in TV-series, which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand, retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named compact video code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patch-level, and proceed to propose a novel hierarchical video representation named spatial pyramid covariance along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over the state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.

Accurate prediction of the visual attributes is significant in various recognition tasks. For many visual attributes, while it is very difficult to describe the exact degrees of their presences, by comparing the pairs of samples, the relative ordering of presences may be easily figured out. Based on this observation, instead of considering such attribute as binary attribute, the relative attribute method learns a ranking function for each attribute to provide more accurate and informative prediction results. In this paper, we also explore pairwise ranking for visual attribute prediction and propose to improve the relative attribute method in two aspects. First, we propose a relative tree method, which can achieve more accurate ranking in case of nonlinearly distributed visual data. Second, by resorting to randomization and ensemble learning, the relative tree method is extended to the relative forest method to further boost the accuracy and simultaneously reduce the computational cost. To validate the effectiveness of the proposed methods, we conduct extensive experiments on four databases: PubFig, OSR, FGNET, and WebFace. The results show that the proposed relative forest method not only outperforms the original relative attribute method, but also achieve the state-of-the-art accuracy for ordinal visual attribute prediction.

Facial expression is a temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e., expressionlet. Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) formed by dense low-level features; 2) a universal manifold model (UMM) is learned over all low-level features and represented as a set of local modes to statistically unify all the STMs; and 3) the local modes on each STM can be instantiated by fitting to the UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode. With the above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and FERA. In all cases, our method outperforms the known state of the art by a large margin.

Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the labels of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.

(a) (b) Figure 1: Illustration of constructing the coupled dictionaries. The red rectangles in (a) and (b) represent the target bounding boxes. The green squares in (a), which are generated by sliding windows outside the target bounding box, correspond to basis patches involved in the background dictionary N. The blue squares in (a) are generated inside the target bounding box in a similar way, which constitute the noisy target dictionary P.

In this paper, we introduce metric learning into Sign Language Recognition(SLR) for the first time and propose a signer adaption framework to address signer-independent SLR. For adapting the general model to the new signer, both clustering and manifold constraints are considered in the adaptive distance metric optimization. The contribution of our work mainly lies in three-folds. Firstly, a Weakly Supervised Metric Learning(WSML) framework is proposed, which combines the clustering and manifold constraints simultaneously. Secondly, the general framework is applied to signer adaptation and achieves good performance. Thirdly, a fragment based feature is designed for sign language representation and the effectiveness is verified in large vocabulary datasets. Our proposed WSML framework can be decomposed into two key steps. The first one is to learn a generic metric from the given labeled data. Then the second step is to realize the distance metric adaptation by considering the clustering and manifold constraints with the unlabeled data. To learn a generic distance metric, the labeled data are used under clustering assumption with classical large margin hinge loss. Specifically, the distances between data points within the same cluster(with same label) should be minimized and the distances between data points from different clusters(with different labels) should be maximized. Here we define the index set with same labels as Sg = {(i, j)|yi = y j,xi,x j ∈ Xl} and the index triplet Bg = {(i, j,k)|yi = y j,yi 6= yk,xi,x j,xk ∈ Xl}. The objective function is

In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. From the perspective of manifold learning, our method can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifold to a lower-dimensional, more discriminative Grassmann manifold where more favorable classification can be achieved. Experiments on several real-world video face datasets demonstrate that the proposed method yields competitive performance compared with the state-of-the-art algorithms.

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching two heterogeneous representations, respectively in Euclidean space and Riemannian manifold. This work makes the first attempt to embed the two heterogeneous spaces into a common discriminant Hamming space. Specifically, we propose Hashing across Euclidean space and Riemannian manifold (HER) by deriving a unified framework to firstly embed the two spaces into corresponding reproducing kernel Hilbert spaces, and then iteratively optimize the intra- and inter-space Hamming distances in a max-margin framework to learn the hash functions for the two spaces. Extensive experiments demonstrate the impressive superiority of our method over the state-of-the-art competitive hash learning methods.

This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. Through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.

In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt model degenerate in target domain, i.e., the test domain. To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE). The proposed BAE attempts to shift source domain samples to target domain, and also shift the target domain samples to source domain. The non-linear transformation of BAE ensures the feasibility of shifting between domains, and the distribution consistency between the shifted domain and the desirable domain is constrained by sparse reconstruction between them. As a result, the shifted source domain is supervised and follows similar distribution as target domain. Therefore, any supervised method can be applied on the shifted source domain to train a classifier for classification in target domain. The proposed method is evaluated on three domain adaptation scenarios of face recognition, i.e., domain adaptation across view angle, ethnicity, and imaging sensor, and the promising results demonstrate that our proposed BAE can shift samples between domains and thus effectively deal with the domain discrepancy.

We address the challenging large-scale content-based face image retrieval problem, intended as searching images based on the presence of specific subject, given one face image of him/her. To this end, one natural demand is a supervised binary code learning method. While the learned codes might be discriminating, people often have a further expectation that whether some semantic message (e.g., visual attributes) can be read from the human-incomprehensible codes. For this purpose, we propose a novel binary code learning framework by jointly encoding identity discriminability and a number of facial attributes into unified binary code. In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on a new purified large-scale web celebrity database, named CFW 60K, with abundant manual identity and attributes annotation, and experimental results exhibit the superiority of our method over state-of-the-art.

Attributes are mid-level semantic properties of objects. Recent research has shown that visual attributes can benefit many traditional learning problems in computer vision community. However, attribute learning is still a challenging problem as the attributes may not always be predictable directly from input images and the variation of visual attributes is sometimes large across categories. In this paper, we propose a unified multiplicative framework for attribute learning, which tackles the key problems. Specifically, images and category information are jointly projected into a shared feature space, where the latent factors are disentangled and multiplied for attribute prediction. The resulting attribute classifier is category-specific instead of being shared by all categories. Moreover, our method can leverage auxiliary data to enhance the predictive ability of attribute classifiers, reducing the effort of instance-level attribute annotation to some extent. Experimental results show that our method achieves superior performance on both instance-level and category-level attribute prediction. For zero-shot learning based on attributes, our method significantly improves the state-of-the-art performance on AwA dataset and achieves comparable performance on CUB dataset.

Facial landmark detection, as a vital topic in computer vision, has been studied for many decades and lots of datasets have been collected for evaluation. These datasets usually have different annotations, e.g., 68-landmark markup for LFPW dataset, while 74-landmark markup for GTAV dataset. Intuitively, it is meaningful to fuse all the datasets to predict a union of all types of landmarks from multiple datasets (i.e., transfer the annotations of each dataset to all other datasets), but this problem is nontrivial due to the distribution discrepancy between datasets and incomplete annotations of all types for each dataset. In this work, we propose a deep regression network coupled with sparse shape regression (DRN-SSR) to predict the union of all types of landmarks by leveraging datasets with varying annotations, each dataset with one type of annotation. Specifically, the deep regression network intends to predict the union of all landmarks, and the sparse shape regression attempts to approximate those undefined landmarks on each dataset so as to guide the learning of the deep regression network for face alignment. Extensive experiments on two challenging datasets, IBUG and GLF, demonstrate that our method can effectively leverage the multiple datasets with different annotations to predict the union of all types of landmarks.

In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.

The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with Log-Euclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the matrix logarithms from the original tangent space to a new tangent space of more discriminability. Under the tangent map framework, the novel metric learning can then be formulated as an optimization problem of seeking a Mahalanobis-like matrix, which can take the advantage of traditional metric learning techniques. Extensive evaluations on several image set classification tasks demonstrate the effectiveness of our proposed metric learning method.

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

Dictionary learning (DL) has now become an important feature learning technique that owns state-of-the-art recognition performance. Due to sparse characteristic of data in real-world applications, DL uses a set of learned dictionary bases to represent the linear decomposition of a data point. Fisher discrimination DL (FDDL) is a representative supervised DL method, which constructs a structured dictionary whose atoms correspond to the class labels. Recent years have witnessed a growing interest in multi-view (more than two views) feature learning techniques. Although some multi-view (or multi-modal) DL methods have been presented, there still exists much room for improvement. How to enhance the total discriminability of dictionaries and reduce their redundancy is a crucial research topic. To boost the performance of multi-view DL technique, we propose an uncorrelated multi-view discrimination DL (UMDDL) approach for recognition. By making dictionary atoms correspond to the class labels such that the obtained reconstruction error is discriminative, UMDDL aims to jointly learn multiple dictionaries with totally favorable discriminative power. Furthermore, we design the uncorrelated constraint for multi-view DL, so as to reduce the redundancy among dictionaries learned from different views. Experiments on several public datasets demonstrate the effectiveness of the proposed approach.

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. The final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean subspace, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.

Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity. Inspired by the observation that pose variations change non-linearly but smoothly, we propose to learn pose-robust features by modeling the complex non-linear transform from the non-frontal face images to frontal ones through a deep network in a progressive way, termed as stacked progressive auto-encoders (SPAE). Specifically, each shallow progressive auto-encoder of the stacked network is designed to map the face images at large poses to a virtual view at smaller ones, and meanwhile keep those images already at smaller poses unchanged. Then, stacking multiple these shallow auto-encoders can convert non-frontal face images to frontal ones progressively, which means the pose variations are narrowed down to zero step by step. As a result, the outputs of the topmost hidden layers of the stacked network contain very small pose variations, which can be used as the pose-robust features for face recognition. An additional attractiveness of the proposed method is that no pose estimation is needed for the test images. The proposed method is evaluated on two datasets with pose variations, i.e., MultiPIE and FERET datasets, and the experimental results demonstrate the superiority of our method to the existing works, especially to those 2D ones.

Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet. Specifically, our method contains three key components: 1) each expression video clip is modeled as a spatio-temporal manifold (STM) formed by dense low-level features, 2) a Universal Manifold Model (UMM) is learned over all low-level features and represented as a set of local ST modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local ST mode. With above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and AFEW. In all cases, our method reports results better than the known state-of-the-art.

In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under real-world environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, and partial least squares, are investigated for comparisons. Finally, an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance. We perform an extensive evaluation on the challenge data (including validation set and blind test set), and evaluate the effects of different strategies in our pipeline. The final recognition accuracy achieved 50.4% on test set, with a significant gain of 16.7% above the challenge baseline 33.7%.

Head-shoulder detection is widely used in many applications, and robust image descriptors are crucial to the detection performance. In this paper, by exploiting the second-order region covariance descriptor as a complement to widely-used histogram-based descriptors, we propose a new two-stage coarse-to-fine cascade framework to make full use of both types of descriptors for robust head-shoulder detection. Specifically, in the first stage, two histogram-based descriptors, i.e., local Histogram of Oriented Gradients (HOG) and histogram of Local Binary Pattern (LBP), are utilized by a Viola-Jones classifier to rapidly reject most non-head-shoulder candidate windows. In contrast, the second stage further boost the performance via multiple kernel learning on Riemannian manifold formed by Region Covariance Matrix (RCM), a second-order statistic descriptor with stronger discriminative power. Experimental results on a public dataset demonstrate that our method improves detection rate significantly with satisfactory detection speed.

Hashing methods that aim to seek a compact binary code for each image are demonstrated to be efficient for scalable content-based image retrieval. In this paper, we propose a new hashing method called semisupervised kernel hyperplane learning (SKHL) for semantic image retrieval by modeling each hashing function as a nonlinear kernel hyperplane constructed from an unlabeled dataset. Moreover, a Fisher-like criterion is proposed to learn the optimal kernel hyperplanes and hashing functions, using only weakly labeled training samples with side information. To further integrate different types of features, we also incorporate multiple kernel learning (MKL) into the proposed SKHL (called SKHL-MKL), leading to better hashing functions. Comprehensive experiments on CIFAR-100 and NUS-WIDE datasets demonstrate the effectiveness of our SKHL and SKHL-MKL.

Domain adaptation has shown promising results in computer vision applications. In this paper, we propose a new unsupervised domain adaptation method called domain adaptation by shifting covariance (DASC) for object recognition without requiring any labeled samples from the target domain. By characterizing samples from each domain as one covariance matrix, the source and target domain are represented into two distinct points residing on a Riemannian manifold. Along the geodesic constructed from the two points, we then interpolate some intermediate points (i.e., covariance matrices), which are used to bridge the two domains. By utilizing the principal components of each covariance matrix, samples from each domain are further projected into intermediate feature spaces, which finally leads to domain-invariant features after the concatenation of these features from intermediate points. In the multiple source domain adaptation task, we also need to effectively integrate different types of features between each pair of source and target domains. We additionally propose an SVM based method to simultaneously learn the optimal target classifier as well as the optimal weights for different source domains. Extensive experiments demonstrate the effectiveness of our method for both single source and multiple source domain adaptation tasks.

Effective characterization of texture images requires exploiting multiple visual cues from the image appearance. The local binary pattern (LBP) and its variants achieve great success in texture description. However, because the LBP(-like) feature is an index of discrete patterns rather than a numerical feature, it is difficult to combine the LBP(-like) feature with other discriminative ones by a compact descriptor. To overcome the problem derived from the nonnumerical constraint of the LBP, this paper proposes a numerical variant accordingly, named the LBP difference (LBPD). The LBPD characterizes the extent to which one LBP varies from the average local structure of an image region of interest. It is simple, rotation invariant, and computationally efficient. To achieve enhanced performance, we combine the LBPD with other discriminative cues by a covariance matrix. The proposed descriptor, termed the covariance and LBPD descriptor (COV-LBPD), is able to capture the intrinsic correlation between the LBPD and other features in a compact manner. Experimental results show that the COV-LBPD achieves promising results on publicly available data sets.

With the quickly increasing of the deaf community, how to communicate with the hearing persons is becoming a serious social problem. Furthermore, the investigation indicates that the deaf community is more self-enclosed and won't exchange ideas with the hearing. To address this challenge, we develop VisualComm, a tool to support communication between deaf and hearing persons with sign language recognition technology by using the Kinect. The main contribution of the system is a holistic solution of a two-way communication between deaf and hearings, and furthermore it is a seamless experience tailored for this particular activity. Currently we have implemented the basic communication based on 370 daily Chinese words for signer.

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.

The Still-to-Video (S2V) face recognition systems typically need to match faces in low-quality videos captured under unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex lighting, and alignment difficulty. To address the problem, one solution is to select the frames of `best quality' from videos (hereinafter called quality alignment in this paper). Meanwhile, the faces in the selected frames should also be geometrically aligned to the still faces offline well-aligned in the gallery. In this paper, we discover that the interactions among the three tasks-quality alignment, geometric alignment and face recognition-can benefit from each other, thus should be performed jointly. With this in mind, we propose a Coupling Alignments with Recognition (CAR) method to tightly couple these tasks via low-rank regularized sparse representation in a unified framework. Our method makes the three tasks promote mutually by a joint optimization in an Augmented Lagrange Multiplier routine. Extensive experiments on two challenging S2V datasets demonstrate that our method outperforms the state-of-the-art methods impressively.

In this paper, we propose a novel cascaded face shape space pruning algorithm for robust facial landmark detection. Through progressively excluding the incorrect candidate shapes, our algorithm can accurately and efficiently achieve the globally optimal shape configuration. Specifically, individual landmark detectors are firstly applied to eliminate wrong candidates for each landmark. Then, the candidate shape space is further pruned by jointly removing incorrect shape configurations. To achieve this purpose, a discriminative structure classifier is designed to assess the candidate shape configurations. Based on the learned discriminative structure classifier, an efficient shape space pruning strategy is proposed to quickly reject most incorrect candidate shapes while preserve the true shape. The proposed algorithm is carefully evaluated on a large set of real world face images. In addition, comparison results on the publicly available BioID and LFW face databases demonstrate that our algorithm outperforms some state-of-the-art algorithms.

We propose a novel salient region detection algorithm by texture-suppressed background contrast. We employ a structure extraction algorithm to suppress the small scale textures which are supposed to be not sensitive for human vision system. Then the texture-suppressed image is segmented into homogeneous superpixels. Motivated by the observation that the spatial distribution of the background has a high probability on the boundaries of images, we estimate the background as superpixels near the image boundaries. The saliency of each superpixel is then defined as the summation of its k minimum color distances to the estimated background superpixels. Finally a post-processing process involving spatial and color adjacency is employed to generate a per-pixel saliency map. Experimental results demonstrate that the proposed method outperforms the state-of-the-art approaches.

Real-world nonlinear dynamic textures (DTs) usually consist of temporally multiple linear DTs which cannot be correctly modeled by previous works. In this paper, we propose piecewise linear dynamic systems (PLDS) to model temporally multiple DTs. PLDS simultaneously decides the temporal segmentation, models each DT segment with an LDS and the whole DT by switching between the LDS'. Experimental results verify that PLDS can capture the stochastic and dynamic nature of temporally multiple DTs and it synthesizes nonlinear DTs without decay or divergence. An EM-like algorithm iterating between sequence division and LDS' fitting is adopted to learn the model parameters.

Canonical correlation analysis (CCA) based methods achieve great success for pose alignment. However, CCA has limitations as a linear and global algorithm. Although some variants have been proposed to overcome the limitations, neither of them achieves locality and nonlinearity at the same time. In this paper, we propose a novel algorithm called Instance-Specific Canonical Correlation Analysis (ISCCA), which approximates the nonlinear data by computing the instance specific projections along the smooth curve of the manifold. Based on the framework of least squares regression, CCA is extended to the instance-specific case which obtains a set of locally-linear smooth but globally-nonlinear transformations. The optimization problem is proved to be convex and could be solved efficiently by alternating optimization. And the globally optimal solutions could be achieved with theoretical guarantee. Experimental results for pose alignment demonstrate the effectiveness of our proposed method.

In this paper, we propose a method for video-based human emotion recognition. For each video clip, all frames are represented as an image set, which can be modeled as a linear subspace to be embedded in Grassmannian manifold. After feature extraction, Class-specific One-to-Rest Partial Least Squares (PLS) is learned on video and audio data respectively to distinguish each class from the other confusing ones. Finally, an optimal fusion of classifiers learned from both modalities (video and audio) is conducted at decision level. Our method is evaluated on the Emotion Recognition In The Wild Challenge (EmotiW 2013). The experimental results on both validation set and blind test set are presented for comparison. The final accuracy achieved on test set outperforms the baseline by 26%.

This paper presents a set of effective and efficient features, namely strip features, for detecting objects in real-scene images. Although shapes of a specific class usually have large intraclass variance, some basic local shape elements are relatively stable. Based on this observation, we propose a set of strip features to describe the appearances of those shape elements. Strip features capture object shapes with edgelike and ridgelike strip patterns, which significantly enrich the efficient features such as Haar-like and edgelet features. The proposed features can be efficiently calculated via two kinds of approaches. Moreover, the proposed features can be extended to a perturbed version (namely, perturbed strip features) to alleviate the misalignment caused by deformations. We utilize strip features for object detection under an improved boosting framework, which adopts a complexity-aware criterion to balance the discriminability and efficiency for feature selection. We evaluate the proposed approach for object detection on the public data sets, and the experimental results show the effectiveness and efficiency of the proposed approach.

In this paper, we propose a new scheme called Prototype Hyperplane Learning (PHL) for face verification in the wild using only weakly labeled training samples (i.e., we only know whether each pair of samples are from the same class or different classes without knowing the class label of each sample) by leveraging a large number of unlabeled samples in a generic data set. Our scheme represents each sample in the weakly labeled data set as a mid-level feature with each entry as the corresponding decision value from the classification hyperplane (referred to as the prototype hyperplane) of one Support Vector Machine (SVM) model, in which a sparse set of support vectors is selected from the unlabeled generic data set based on the learnt combination coefficients. To learn the optimal prototype hyperplanes for the extraction of mid-level features, we propose a Fisher’s Linear Discriminant-like (FLD-like) objective function by maximizing the discriminability on the weakly labeled data set with a constraint enforcing sparsity on the combination coefficients of each SVM model, which is solved by using an alternating optimization method. Then, we use the recent work called Side-Information based Linear Discriminant (SILD) analysis for dimensionality reduction and a cosine similarity measure for final face verification. Comprehensive experiments on two data sets, Labeled Faces in the Wild (LFW) and YouTube Faces, demonstrate the effectiveness of our scheme.

Video-based Face Recognition (VFR) can be converted to the matching of two image sets containing face images captured from each video. For this purpose, we propose to bridge the two sets with a reference image set that is well-defined and pre-structured to a number of local models offline. In other words, given two image sets, as long as each of them is aligned to the reference set, they are mutually aligned and well structured. Therefore, the similarity between them can be computed by comparing only the corresponded local models rather than considering all the pairs. To align an image set with the reference set, we further formulate the problem as a quadratic programming. It integrates three constrains to guarantee robust alignment, including appearance matching cost term exploiting principal angles, geometric structure consistency using affine invariant reconstruction weights, smoothness constraint preserving local neighborhood relationship. Extensive experimental evaluations are performed on three databases: Honda, MoBo and YouTube. Compared with competing methods, our approach can consistently achieve better results.

Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (SSLDA), where the projecting vectors are not only constrained to sparsity but also structured with a pre-specified set of shapes. While the sparse priors deal with small sample size problem, the proposed structure regularization can also encode higher-order information with better interpretability. We also propose a simple and efficient optimization algorithm to solve the proposed optimization problem. Experiments on face images show the benefits of the proposed structured sparse LDA on both classification accuracy and interpretability.

The goal of image authentication is to accept content-preserving operations and reject content-altering manipulations. So,it is increasingly approached by extracting content-based invariant features from original images and verifying their preservation in received images at later times. Since sparsity usually implies invariance, sparse feature representation has drawn significant attention from the research community. But only if discrimination is also found with a sparse feature, can it be successfully applied in image authentication. This paper proposes a sparse feature for image authentication by exploring the biologically-motivated standard model. Experimental results demonstrate both robustness and discrimination of the feature, and its effectiveness in tamper detection and location as well.

Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.

In this paper, we address the problem of classifying image sets for face recognition, where each set contains images belonging to the same subject and typically covering large variations. By modeling each image set as a manifold, we formulate the problem as the computation of the distance between two manifolds, called manifold-manifold distance (MMD). Since an image set can come in three pattern levels, point, subspace, and manifold, we systematically study the distance among the three levels and formulate them in a general multilevel MMD framework. Specifically, we express a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrate the distances between pairs of subspaces from one of the involved manifolds. We theoretically and experimentally study several configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition with image sets, where identification is achieved by seeking the minimum MMD from the probe to the gallery of image sets. Our experiments demonstrate that, as a general set similarity measure, MMD consistently outperforms other competing nondiscriminative methods and is also promisingly comparable to the state-of-the-art discriminative methods.

In many real-world applications, the same object may have different observations (or descriptions) from multiview observation spaces, which are highly related but sometimes look different from each other. Conventional metric-learning methods achieve satisfactory performance on distance metric computation of data in a single-view observation space, but fail to handle well data sampled from multiview observation spaces, especially those with highly nonlinear structure. To tackle this problem, we propose a new method calledMultiview Metric Learning with Global consistency and Local smoothness(MVML-GL) under a semisupervised learning setting, which jointly considers global consistency and local smoothness. The basic idea is to reveal the shared latent feature space of the multiview observations by embodying global consistency constraints and preserving local geometric structures. Specifically, this framework is composed of two main steps. In the first step, we seek a global consistent shared latent feature space, which not only preserves the local geometric structure in each space but also makes those labeled corresponding instances as close as possible. In the second step, the explicit mapping functions between the input spaces and the shared latent space are learned via regularized locally linear regression. Furthermore, these two steps both can be solved by convex optimizations in closed form. Experimental results with application to manifold alignment on real-world datasets of pose and facial expression demonstrate the effectiveness of the proposed method.

In recent years, face recognition in the unconstrained environment has attracted increasing attentions, and a few methods have been evaluated on the Labeled Faces in the Wild (LFW) database. In the unconstrained conditions, sometimes we cannot obtain the full class label information of all the subjects. Instead we can only get the weak label information, such as the side-information, i.e., the image pairs from the same or different subjects. In this scenario, many multi-class methods (e.g., the well-known Fisher Linear Discriminant Analysis (FLDA)), fail to work due to the lack of full class label information. To effectively utilize the side-information in such case, we propose Side-Information based Linear Discriminant Analysis (SILD), in which the within-class and between-class scatter matrices are directly calculated by using the side-information. Moreover, we theoretically prove that our SILD method is equivalent to FLDA when the full class label information is available. Experiments on LFW and FRGC databases support our theoretical analysis, and SILD using multiple features also achieve promising performance when compared with the state-of-the-art methods.

Image authentication is usually approached by checking the preservation of some invariant features, which are expected to be both robust and discriminative so that content-preserving operations are accepted while content-altering manipulations are rejected. However, most of existing features have not obtained convincing performance due to insufficiency of experiments and over biasing of robustness. Motivated by the sparse coding strategy discovered in primary visual cortex, we explore the possibility of using sparse coding coefficients for image authentication. Through extensive experiments, we discover that the proposed feature bears great discrimination as well as robustness, which indicates the effectiveness of sparse coding as a new invariant feature for image authentication.

In this paper, we present a pose based approach for locating and recognizing human actions in videos. In our method, human poses are detected and represented based on deformable part model. To our knowledge, this is the first work on exploring the effectiveness of deformable part models in combining human detection and pose estimation into action recognition. Comparing with previous methods, ours have three main advantages. First, our method does not rely on any assumption on video preprocessing quality, such as satisfactory foreground segmentation or reliable tracking; Second, we propose a novel compact representation for human pose which works together with human detection and can well represent the spatial and temporal structures inside an action; Third, with human detection taken into consideration in our framework, our method has the ability to locate and recognize multiple actions in the same scene. Experiments on benchmark datasets and recorded cluttered videos verified the efficacy of our method.

In this paper, a novel Sparsely Encoded Local Descriptor (SELD) is proposed for face recognition. Compared with K-means or Random-projection tree based previous methods, sparsity constraint is introduced in our dictionary learning and sequent image encoding, which implies more stable and discriminative face representation. Sparse coding also leads to an image descriptor of summation of sparse coefficient vectors, which is quite different from existing code-words appearance frequency(/histogram)-based descriptors. Extensive experiments on both FERET and challenging LFW database show the effectiveness of the proposed SELD method. Especially on the LFW dataset, recognition accuracy comparable to the best known results is achieved.

Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem.

In previous works of face recognition, similarity between faces is measured by comparing corresponding face regions. That is to say, matching eyes with eyes and mouths with mouths etc.. In this paper, we propose that face can be also recognized by matching non-corresponding facial regions. In another word face can be recognized by matching eyes with mouths, for example. Specifically, the problem we study in this paper can be formulated as how to measure the possibility whether two non-corresponding face regions belong to the same face. We propose that the possibility can be measured via canonical correlation analysis. Experimental results show that it is feasible to recognize face via non-corresponding region matching. The proposed method provides an alternative and more flexible way to recognize faces.

As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo, (2) our synthesized sketches presents more pencil sketch texture.

Automatic object detection for an arbitrary class is an important but very challenging problem, due to the countless kinds of objects in the world and the large amount of labeling work for each object. In this work, we target at solving the problem of automatic object detection for an arbitrary class without the laborious human effort. Motivated by the explosive growth of Web images and the phenomenal success of search techniques, we develop an unsupervised object detection framework by automatically training the object detector on the top returns of certain image search engine queried by the name of the object class. In order to automatically isolate the objects from the Web images for training, only clipart images with simple background are used, which preserve most of the shape information of the objects. A two-stage shape-based clustering algorithm is proposed to mine typical shapes of the object, in which the inner-class variance of object shapes is considered and undesired images are filtered out. In order to reduce the gap between clipart images and real-world images, we introduce an efficient algorithm to synthesize the real-world images from clipart images, and only shape feature is used in the detector training part. Finally, the synthetic images could be used to train object detectors by an off-the-shelf discriminative algorithm, e.g., boosting or SVM. Extensive experiments show the effectiveness of the proposed framework on objects with simple and representative shapes, and the proposed framework could be considered as a good beginning of solving this challenging problem.

Over the past few decades, dimensionality reduction has been widely exploited in computer vision and pattern analysis. This paper proposes a simple but effective nonlinear dimensionality reduction algorithm, named Maximal Linear Embedding (MLE). MLE learns a parametric mapping to recover a single global low-dimensional coordinate space and yields an isometric embedding for the manifold. Inspired by geometric intuition, we introduce a reasonable definition of locally linear patch, Maximal Linear Patch (MLP), which seeks to maximize the local neighborhood in which linearity holds. The input data are first decomposed into a collection of local linear models, each depicting an MLP. These local models are then aligned into a global coordinate space, which is achieved by applying MDS to some randomly selected landmarks. The proposed alignment method, called Landmarks-based Global Alignment (LGA), can efficiently produce a closed-form solution with no risk of local optima. It just involves some small-scale eigenvalue problems, while most previous aligning techniques employ time-consuming iterative optimization. Compared with traditional methods such as ISOMAP and LLE, our MLE yields an explicit modeling of the intrinsic variation modes of the observation data. Extensive experiments on both synthetic and real data indicate the effectivity and efficiency of the proposed algorithm.

We present an algorithm to compute the connected components of upper or lower level sets from level lines. The running time is O (n + |τ|), where n is the size of the image and |τ| is the number of level lines.

Fisher's linear discriminant (FLD) is one of the most widely used linear feature extraction method, especially in many visual computation tasks. Based on the analysis on several limitations of the traditional FLD, this paper attempts to propose a new computational paradigm for discriminative linear feature extraction, named "classifiability-based discriminatory projection pursuit" (CDPP), which is different from the traditional FLD and its variants. There are two steps in the proposed CDPP: one is the construction of a candidate projection set (CPS), and the other is the pursuit of discriminatory projections. Specifically, in the former step, candidate projections are generated by using the nearest between-class boundary samples, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the CPS. We show that the new "projection pursuit" paradigm not only does not suffer from the limitations of the traditional FLD but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experiments on both synthetic and real datasets validate the effectiveness of CDPP for discriminative linear feature extraction.

This paper presents an integrated face image fusion framework, which combines a hierarchical compositional paradigm with seamless image-editing techniques, for gender conversion. In our framework a high-resolution face is represented by a probabilistic graphical model that decomposes a human face into several parts (facial components) constrained by explicit spatial configurations (relationships). Benefiting from this representation, the proposed fusion strategy is able to largely preserve the face identity of each facial component while applying gender transformation. Given a face image, the basic idea is to select reference facial components from the opposite-gender group as templates and transform the appearance of the given image toward the selected facial components. Our fusion approach decomposes a face image into two parts-sketchable and nonsketchable ones. For the sketchable regions (e.g., the contours of facial components and wrinkle lines, etc.), we use a graph-matching algorithm to find the best templates and transform the structure (shape), while for the nonsketchable regions (e.g., the texture area of facial components, skin, etc.), we learn active appearance models and transform the texture attributes in the corresponding principal component analysis space. Both objective and subjective quantitative evaluation results on 200 Asian frontal-face images selected from the public Lotus Hill Image database show that the proposed approach is able to give plausible gender conversion results.

Hand segmentation is the basis of many vision-based hand gesture applications in human computer interaction (HCI). This paper proposes a novel method of skin color weighted disparity competition to incorporate the skin color and depth cues for hand segmentation in clutter environment from stereo camera. The basic idea is that the hand disparity will be apparently different from most of the background disparities. Note that we refer to the scene except the user as the background. The proposed method validates a hand pixel by verifying whether the pixel satisfies the disparity limit of the hand more than of the background. Its novelty lies in incorporating the skin color probability to weight the disparity competition to improve the robustness of validation and the accuracy of boundary location. Experiments demonstrate the efficiency of the proposed method.

In this paper, we propose a novel manifold alignment method by learning the underlying common manifold with supervision of corresponding data pairs from different observation sets. Different from the previous algorithms of semi-supervised manifold alignment, our method learns the explicit corresponding projections from each original observation space to the common embedding space everywhere. Benefiting from this property, our method could process new test data directly rather than re-alignment. Furthermore, our approach doesn’t have any assumption on the data structures, thus it could handle more complex cases and get better results compared with previous work. In the proposed algorithm, manifold alignment is formulated as a minimization problem with proper constraints, which could be solved in an analytical manner with closed-form solution. Experimental results on pose manifold alignment of different objects and faces demonstrate the effectiveness of our proposed method.

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

Today's camera sensors usually have a high gray-scale resolution, e.g. 256, however, due to the dramatic lighting variations, the gray-scales distributed to the face region might be far less than 256. Therefore, besides low spatial resolution, a practical face recognition system must also handle degraded face images of low gray-scale resolution (LGR). In the last decade, low spatial resolution problem has been studied prevalently, but LGR problem was rarely studied. Aiming at robust face recognition, this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly. Possible solutions to the problem are discussed and grouped into three categories: gray-scale resolution invariant features, gray-scale degradation modeling and Gray-scale Super-Resolution (GSR). Then, we propose a Coupled Subspace Analysis (CSA) based GSR method to recover the high gray-scale resolution image from a single input LGR image. Extensive experiments on FERET and CMU-PIE face databases show that the proposed method can not only dramatically increase the gray-scale resolution and visualization quality, but also impressively improve the accuracy of face recognition.

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.

Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with variable-location and variable-size blocks to capture salient characteristics of the object structure. We consider a CoHOG feature as a block with a special pattern described by the offset. A boosting algorithm is further introduced to select the appropriate locations and offsets to construct an efficient and accurate cascade classifier. Experimental results on public datasets show that our approach simultaneously achieves high accuracy and fast speed on both pedestrian detection and car detection tasks.

This paper proposes a coarse-to-fine strategy to detect human actions in the realistic videos given a single example of such action. The proposed method is learning-free and doesn't require any prior knowledge. Input video is separated into a batch of spatio-temporal volumes based on chi-square distance measure of the volumetric features and further identified by contextual motion information. Instead of the exhaustive search, query action is localized by matching local salient geometric features only between itself and the pruned spatio-temporal volumes. The competitive results obtained from the evaluation on a collection of challenging action data indicate the effectiveness and the computational efficiency of our method.

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.

We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common used methods on public datasets in both pedestrian detection and car detection.

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.

In this paper, we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-Or graph, in which And nodes decompose a face into parts to describe details (e.g., hair, wrinkles, etc.) crucial for age perception and Or nodes represent large diversity of faces by alternative selections. Then a face instance is a transverse of the And-Or graph-parse graph. Face aging is modeled as a Markov process on the parse graph representation. We learn the parameters of the dynamic model from a large annotated face data set and the stochasticity of face aging is modeled in the dynamics explicitly. Based on this model, we propose a face aging simulation and prediction algorithm. Inversely, an automatic age estimation algorithm is also developed under this representation. We study two criteria to evaluate the aging results using human perception experiments: 1) the accuracy of simulation: whether the aged faces are perceived of the intended age group, and 2) preservation of identity: whether the aged faces are perceived as the same person. Quantitative statistical analysis validates the performance of our aging model and age estimation algorithm.

Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these methods usually have limited performance because the target of SR is not absolutely consistent with that of face recognition. Moreover, time-consuming sophisticated SR algorithms are not suitable for real-time applications. To avoid these limitations, we propose a novel approach for LR face recognition without any SR preprocessing. Our method based on coupled mappings (CMs), projects the face images with different resolutions into a unified feature space which favors the task of classification. These CMs are learned through optimizing the objective function to minimize the difference between the correspondences (i.e., low-resolution image and its high-resolution counterpart). Inspired by locality preserving methods for dimensionality reduction, we introduce a penalty weighting matrix into our objective function. Our method significantly improves the recognition performance. Finally, we conduct experiments on publicly available databases to verify the efficacy of our algorithm.

This letter presents a novel object tracking approach within the Bayesian inference framework through implicit online learning. In our approach, the target is represented by multiple patches, each of which is encoded by a powerful and efficient region descriptor called Sigma set. To model each target patch, we propose to utilize the online one-class support vector machine algorithm, named Implicit online Learning with Kernels Model (ILKM). ILKM is simple, efficient, and capable of learning a robust online target predictor in the presence of appearance changes. Responses of ILKMs related to multiple target patches are fused by an arbitrator with an inference of possible partial occlusions, to make the decision and trigger the model update. Experimental results demonstrate that the proposed tracking approach is effective and efficient in ever-changing and cluttered scenes.

Sign language recognition systems suffer from the problem of signer dependence. In this letter, we propose a novel method that adapts the original model set to a specific signer with his/her small amount of training data. First, affinity propagation is used to extract the exemplars of signer independent hidden Markov models; then the adaptive training vocabulary can be automatically formed. Based on the collected sign gestures of the new vocabulary, the combination of maximum a posteriori and iterative vector field smoothing is utilized to generate signer-adapted models. Experimental results on six signers demonstrate that the proposed method can reduce the amount of the adaptation data and still can achieve high recognition performance.

Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we first propose local Gabor XOR patterns (LGXP), which encodes the Gabor phase by using the local XOR pattern (LXP) operator. Then, we introduce block-based Fisher's linear discriminant (BFLD) to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power. Finally, by using BFLD, we fuse local patterns of Gabor magnitude and phase for face recognition. We evaluate our approach on FERET and FRGC 2.0 databases. In particular, we perform comparative experimental studies of different local Gabor patterns. We also make a detailed comparison of their combinations with BFLD, as well as the fusion of different descriptors by using BFLD. Extensive experimental results verify the effectiveness of our LGXP descriptor and also show that our fusion approach outperforms most of the state-of-the-art approaches.

Linear Discriminant Analysis (LDA) is a popular method for dimensionality reduction and classification. In real-world applications when there is no sufficient labeled data, LDA suffers from serious performance drop or even fails to work. In this paper, we propose a novel method called Spectral Transduction Semi-Supervised Discriminant Analysis (STSDA), which can alleviate such problem by utilizing both labeled and unlabeled data. Our method takes into consideration both label augmenting and local structure preserving. First, we formulate label transduction with labeled and unlabeled data as a constrained convex optimization problem and solve it efficiently with a closed-form solution by using orthogonal projector matrices. Then, unlabeled data with reliable class estimations are selected with a balanced strategy to augment the original labeled data set. At last, LDA with manifold regularization is performed. Experimental results on face recognition demonstrate the effectiveness of our proposed method.

Given an image region of pixels, second order statistics can be used to construct a descriptor for object representation. One example is the covariance matrix descriptor, which shows high discriminative power and good robustness in many computer vision applications. However, operations for the covariance matrix on Riemannian manifolds are usually computationally demanding. This paper proposes a novel second order statistics based region descriptor, named “Sigma Set”, in the form of a small set of vectors, which can be uniquely constructed through Cholesky decomposition on the covariance matrix. Sigma Set is of low dimension, powerful and robust. Moreover, compared with the covariance matrix, Sigma Set is not only more efficient in distance evaluation and average calculation, but also easier to be enriched with first order statistics. Experimental results in texture classification and object tracking verify the effectiveness and efficiency of this novel object descriptor.

The variations of pose lead to significant performance decline in face recognition systems, which is a bottleneck in face recognition. A key problem is how to measure the similarity between two image vectors of unequal length that viewed from different pose. In this paper, we propose a novel approach for pose robust face recognition, in which the similarity is measured by correlations in a media subspace between different poses on patch level. The media subspace is constructed by Canonical Correlation Analysis, such that the intra-individual correlations are maximized. Based on the media subspace two recognition approaches are developed. In the first, we transform non-frontal face into frontal for recognition. And in the second, we perform recognition in the media subspace with probabilistic modeling. The experimental results on FERET database demonstrate the efficiency of our approach.

This paper proposes a novel descriptor, granularity-tunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular uncertainty of the line segments in the Hough space. Then this uncertainty is backprojected into the image space by orientation-space partitioning to achieve efficient implementation. By changing the granularity parameter, the level of uncertainty can be controlled quantitatively. Therefore a family of descriptors with versatile representation property can be generated. Specifically, the finely granular GGP descriptors can represent the specific geometry information of the object (the same as Edgelet); while the coarsely granular GGP descriptors can provide the statistical representation of the object (the same as histograms of oriented gradients, HOG). Moreover, the position, orientation, strength and distribution of the gradients are embedded into a unified descriptor to further improve the GGP's representation power. A cascade structured classifier is built by boosting the linear regression functions. Experimental results on INRIA dataset show that the proposed method achieves comparable results to those of the state-of-the-art methods.