Current 3D visual grounding tasks only process sentence-level detection or segmentation, which critically fails to leverage the rich compositional contextual reasonings within natural language expressions. To address this challenge, we introduce Detailed 3D Referring Expression Segmentation (3D-DRES), a new task that provides a phrase to 3D instance mapping, aiming at enhancing fine-grained 3D vision-language understanding. To support 3D-DRES, we present DetailRefer, a new dataset comprising 55,432 descriptions spanning 11,054 distinct objects. Unlike previous datasets, DetailRefer implements a pioneering phrase-instance annotation paradigm where each referenced noun phrase is explicitly mapped to its corresponding 3D elements. Additionally, we introduce DetailBase, a purposefully streamlined yet effective baseline architecture that supports dual-mode segmentation at both sentence and phrase levels. Our experimental results demonstrate that models trained on DetailRefer not only excel at phrase-level segmentation but also show surprising improvements on traditional 3D-RES benchmarks.

Visual anomaly detection is limited by the lack of sufficient anomaly data. While existing anomaly synthesis methods have made remarkable progress, achieving both realism and diversity in synthesis remains a major obstacle. To address this, we propose AnomalyPainter, a novel framework that breaks the diversity-realism trade-off dilemma through synergizing Vision Language Large Model (VLLM), Latent Diffusion Model (LDM), and our newly introduced texture library Tex-9K. Tex-9K is a professional texture library containing 75 categories and 8792 texture assets crafted for diverse anomaly synthesis. Leveraging VLLM's general knowledge, reasonable anomaly text descriptions are generated for each industrial object and matched with relevant diverse textures from Tex-9K. These textures then guide the LDM via ControlNet to paint on normal images. Furthermore, we introduce Texture-Aware Latent Init to stabilize the natural-image-trained ControlNet for industrial images. Extensive experiments show that AnomalyPainter outperforms existing methods in realism, diversity, and generalization, achieving superior downstream performance.

Although fully-supervised oriented object detection has made significant progress in remote sensing image understanding, it comes at the cost of labor-intensive annotation. Recent studies have explored weakly and semi-supervised learning to alleviate this burden. However, these methods overlook the difficulties posed by dense annotations in complex remote sensing scenes. In this paper, we introduce a novel setting called sparsely annotated oriented object detection (SAOOD), which only labels partial instances, and propose a solution to address its challenges. Specifically, we focus on two key issues in the setting: (1) sparse labeling leading to overfitting on limited foreground representations, and (2) unlabeled objects (false negatives) confusing feature learning. To this end, we propose the S2Teacher, a novel angle-consistency guided method that progressively mines pseudo-labels for unlabeled objects from easy to hard, enhancing foreground representations. Additionally, it reweights the loss of unlabeled objects to mitigate their impact during training. Extensive experiments demonstrate that S2Teacher not only significantly improves detector performance across different sparse annotation levels but also achieves near-fully-supervised performance on the DOTA dataset with only 10% annotation instances, effectively balancing accuracy and labeling cost.

Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data alongside large-scale unlabeled data. A major challenge in SSIS lies in the inherent noise of pseudo-labels, particularly when class and mask qualities are coupled into a single confidence score for filtering. Such coupling often results in sub-optimal trade-offs between semantic accuracy and spatial precision. To address this, we propose a novel Pseudo-Label Decoupling and Correction (PL-DC) framework, which explicitly decouples and enhances the pseudo-label selection process for SSIS. At the instance level, we introduce a Decoupled Filtering with Adaptive Class-Aware Thresholds mechanism, which independently evaluates class and mask qualities using category-specific thresholds updated via exponential moving averages. At the category level, we design a Dynamic Instance Category Correction module that reassigns ambiguous class pseudo-label by leveraging semantic prototypes and consistency alignment. At the pixel level, a Pixel-Level Mask Uncertainty-Aware mechanism is applied to suppress the influence of unreliable pixels during mask supervision, further improving the robustness against pixel-wise noise. Extensive experiments on COCO and Cityscapes datasets demonstrate that the proposed PL-DC achieves significant performance improvements, setting new state-of-the-art results. Notably, PL-DC achieves gains of +11.7 mAP with just 1% labeled COCO data and +16.4 mAP with 5% Cityscapes labels, showing its effectiveness under extremely low-label regimes.

Reconstructing 3D objects from a single image is a long-standing challenge, particularly under real-world occlusions. While recent diffusion-based view synthesis models can generate consistent novel views from a single RGB image, they generally assume fully visible inputs and struggle when parts of the object are occluded, leading to inconsistent views and degraded 3D reconstruction quality. To address this limitation, we propose DeOcc-1-to-3, an end-to-end framework for occlusion-aware multi-view generation. Our method directly synthesizes six structurally consistent novel views from a single partially occluded image, enabling downstream 3D reconstruction without requiring prior inpainting or manual annotations. We design a self-supervised training pipeline that leverages occluded–unoccluded image pairs and pseudo-ground-truth views to guide structure-aware completion and view consistency. Without modifying the original architecture, we fully fine-tune the diffusion model to jointly learn completion and multi-view generation. Additionally, we introduce the first benchmark for occlusion-aware reconstruction, covering diverse occlusion levels, object categories, and mask patterns, providing a standardized evaluation protocol.

Referring Image Segmentation (RIS), which aims to segment specific objects based on natural language descriptions, plays an essential role in vision-language understanding. Despite its progress in remote sensing applications, RIS under Low-Altitude Drone (LAD) scenarios remains underexplored, as existing datasets and methods are typically designed for high-altitude and static-view imagery. They struggled to handle the unique characteristics of LAD views, such as diverse viewpoints and high object density. In this paper, we propose RIS-LAD, the first fine-grained RIS benchmark tailored for LAD scenarios, featuring 13,871 meticulously annotated image-text-mask triplets collected from real-world drone footage with emphasis on small, densely cluttered objects and multi-view perspectives. Additionally, we propose the Semantic-Aware Adaptive Reasoning Network, which decomposes and adaptively routes semantic information to different network stages rather than uniformly injecting all linguistic features. Specifically, the Category-Dominated Linguistic Enhancement aligns visual features with object categories during early encoding, while the Adaptive Reasoning Fusion Module dynamically selects semantic cues across scales to enhance reasoning in complex scenes. Extensive experiments reveal that RIS-LAD presents substantial challenges to state-of-the-art RIS algorithms, and also demonstrate the effectiveness of our proposed model in addressing these challenges.

This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models. It operates without the need for training, offering a distinct advantage over existing diffusion methods that transfer style across entire videos. The endeavors of this paper comprise: (1) A point-matching mask propagation strategy that leverages the feature maps from the DDIM inversion. This streamlines the model's architecture by obviating the need for tracking models. (2) A training-free AdaIN-guided localized video stylization mechanism that operates at both the latent and attention levels. This balances content fidelity and style richness, mitigating the loss of localized details commonly associated with direct video stylization. (3) A sliding-window consistent smoothing scheme that harnesses optical flow within the pixel representation and refines predicted noise to update the latent space. This significantly enhances temporal consistency and diminishes artifacts in stylized video. Our proposed UniVST has been validated to be superior to existing methods in quantitative and qualitative metrics. It adeptly addresses the challenges of preserving the primary object's style while ensuring temporal consistency and detail preservation.

Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner where the learned prompt vectors are shared across all categories. Consequently, the tailored prompts often fail to discern class-specific visual concepts, thereby hindering the transferred performance for classes that share similar or complex visual attributes. Recent advances mitigate this challenge by leveraging external knowledge from Large Language Models (LLMs) to furnish class descriptions, yet incurring notable inference costs. In this paper, we introduce TextRefiner, a plug-and-play method to refine the text prompts of existing methods by leveraging the internal knowledge of VLMs. Particularly, TextRefiner builds a novel local cache module to encapsulate fine-grained visual concepts derived from local tokens within the image branch. By aggregating and aligning the cached visual descriptions with the original output of the text branch, TextRefiner can efficiently refine and enrich the learned prompts from existing methods without relying on any external expertise. For example, it improves the performance of CoOp from 71.66% to 76.96% on 11 benchmarks, surpassing CoCoOp which introduced instance-wise feature for text prompts. Equipped with TextRefiner, PromptKD achieves state-of-the-art performance while keep inference efficient.

In the Blind Image Quality Assessment (BIQA) field, accurately assessing the quality of authentically distorted images presents a substantial challenge due to the diverse distortion types in natural settings. Existing state-of-the-art IQA methods mix a sequence of distortions into entire images to establish global distortion priors, but are inadequate for authentic images with spatially varied distortions. To address this, we introduce a novel IQA framework that employs knowledge distillation tailored to perceive spatially heterogeneous distortions, enhancing quality-distortion awareness. Specifically, we introduce a novel Block-wise Degradation Modelling approach that applies distinct distortions to different spatial blocks of an image, thereby expanding local distortion priors. Following this, we present a Block-wise Aggregation and Filtering module that enables fine-grained attention to the quality information within different distortion areas of the image. Furthermore, to effectively capture the complex relationships between distortions across different regions while preserving overall quality perception, we introduce Contrastive Knowledge Distillation to enhance the model’s ability to discriminate between different types of distortions and Affinity Knowledge Distillation to model the correlation among distortions in different regions. Extensive experiments on standard BIQA datasets demonstrate the effectiveness and competitiveness of the proposed method.

Representing 3D scenes from multiview images is a core challenge in computer vision and graphics, which requires both precise rendering and accurate reconstruction. Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention for its high-quality rendering and fast inference speed. Yet, due to the unstructured and irregular nature of Gaussian point clouds, ensuring accurate geometry reconstruction remains difficult. Existing methods primarily focus on geometry regularization, with common approaches including primitive-based and dual-model frameworks. However the former suffers from inherent conflicts between rendering and reconstruction, while the latter is computationally and storage-intensive. To address these challenges, we propose CarGS, a unified model leveraging Contribution-adaptive regularization to achieve simultaneous, high-quality rendering and surface reconstruction. The essence of our framework is learning adaptive contribution for Gaussian primitives by squeezing the knowledge from geometry regularization into a compact MLP. Additionally, we introduce a geometry-guided densification strategy with clues from both normals and Signed Distance Fields (SDF) to improve the capability of capturing high-frequency details. Our design improves the mutual learning of the two tasks, meanwhile its unified structure doesn’t require separate models as in dual-model based approaches, guaranteeing efficiency. Extensive experiments demonstrate CarGS’s ability to achieve state-of-the-art (SOTA) results in both rendering fidelity and reconstruction accuracy while maintaining real-time speed and minimal storage size.

Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-labels using fixed strategies and train 1 × 1 convolutional layers as a simple decoder, leading to low performance compared to fully-supervised methods. We emphasize two drawbacks in these approaches: 1). The model is prone to fitting incorrect knowledge due to the pseudo-label containing substantial noise. 2). The simple decoder fails to capture and learn the semantic features of camouflaged objects, especially for small-sized objects, due to the low-resolution pseudo-labels and severe confusion between foreground and background pixels. To this end, we propose a UCOD method with a teacher-student framework via Dynamic Pseudo-label Learning called UCOD-DPL, which contains an Adaptive Pseudo-label Module (APM), a Dual-Branch Adversarial (DBA) decoder, and a Look-Twice mechanism. The APM module adaptively combines pseudo-labels generated by fixed strategies and the teacher model to prevent the model from overfitting incorrect knowledge while preserving the ability for self-correction; the DBA decoder takes adversarial learning of different segmentation objectives, guides the model to overcome the foreground-background confusion of camouflaged objects, and the Look-Twice mechanism mimics the human tendency to zoom in on camouflaged objects and performs secondary refinement on small-sized objects. Extensive experiments show that our method demonstrates outstanding performance, even surpassing some existing fully supervised methods. The code is available now1.

Segment Anything Model (SAM) has achieved remarkable success in the field of class-agnostic image segmentation by utilizing points or boxes as prompts. However, we identify two significant limitations when compared to traditional image segmentation models: (1) Trained in a category-agnostic interactive segmentation manner, SAM lacks the ability to discern object granularity and semantics, rendering it ineffective for traditional instance, semantic, and panoptic segmentation tasks. (2) SAM’s inefficient use of instance-independent visual features and tokens necessitates maintaining unique features and tokens for each instance, leading to excessive GPU memory consumption and diminished segmentation efficiency. To address these issues, we propose the Universal Segment Anything Model (U-SAM), a semantic-aware and memory-efficient segmentation model designed to perform both promptable and traditional segmentation tasks within a compact and unified framework. Specifically, U-SAM enhances SAM by integrating the Multi-Scale Semantic-Aware Image Encoder (S2IE), thus providing multi-scale semantic features for achieving traditional image segmentation tasks. Additionally, U-SAM is equipped with a Twin Token Mask Decoder (T2MD) which reduces GPU memory overhead by substituting replicated visual features with replicated tokens. Extensive experiments across interactive, instance, semantic, and panoptic segmentation demonstrate U-SAM’s promising results. Notably, U-SAM is 9× smaller and 10× faster than SAM, showing strong performance in zero-shot segmentation. Moreover, U-SAM surpasses the SOTA object-prompter-based model, RSPrompter, by achieving a 6.2% increase in PQ, operating 14× faster, and cutting training memory usage by 61%.

The role of high-quality pseudo-labels is pivotal in semi-supervised instance segmentation (SSIS). However, existing SSIS frameworks predominantly produce pseudo-labels at a single resolution, which can introduce noise that adversely affects the quality of learning at both the pixel level and in terms of class discrimination. This paper introduces the Dual-Resolution Pseudo-Label Integration for Semi-Supervised Instance Segmentation (DuPI), a novel framework designed to enhance learning by integrating pseudo-labels derived from dual-resolution inputs. The DuPI framework incorporates a Dual-Resolution Pseudo-Label Correction (DPC) module, which refines pseudo-labels through a process of cross-resolution rectification and fusion. Furthermore, the framework introduces an Area-Adaptive Learning (AAL) strategy aimed at enhancing the quality of pseudo-labels sourced from extra-resolution inputs. The AAL strategy addresses the training challenges associated with small objects at lower resolutions by re-weighting pseudo-labels corresponding to tiny mask areas using Intersection over Union (IoU) metrics from the assignments. Experiments on the COCO and BDD100K datasets demonstrate that DuPI achieves state-of-the-art SSIS performance under various semi-supervised settings.

Vision-Language Models (VLMs) face two critical limitations in visual representation learning: degraded supervision due to information loss during gradient propagation, and the inherent semantic sparsity of textual supervision compared to visual data. We propose the Diffusion Supervision Vision-Language Model (DS-VLM), a plug-and-play framework that introduces diffusion-based direct supervision for vision-language alignment. By reconstructing input images through a diffusion model conditioned on outputs of the visual encoder and the connector, our method establishes a short-path gradient propagation channel from pixel space to visual features. This approach simultaneously preserves high-level semantic alignment through conventional text supervision while enhancing visual feature quality via pixel-level reconstruction constraints. Extensive experiments conducted across various visual encoders and LLMs of different scales demonstrate the effectiveness of our approach.

Multimodal person re-identification (Re-ID) aims to match pedestrian images across different modalities. However, most existing methods focus on limited cross-modal settings and fail to support arbitrary query-retrieval combinations, hindering practical deployment. We propose FlexiReID, a flexible framework that supports seven retrieval modes across four modalities: RGB, infrared, sketches, and text. FlexiReID introduces an adaptive mixture-of-experts (MoE) mechanism to dynamically integrate diverse modality features and a cross-modal query fusion module to enhance multimodal feature extraction. To facilitate comprehensive evaluation, we construct CIRS-PEDES, a unified dataset extending four popular Re-ID datasets to include all four modalities. Extensive experiments demonstrate that FlexiReID achieves state-of-the-art performance and offers strong generalization in complex scenarios.

The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works leverage the semi-supervised COD framework that relies on a small number of labeled data and a large volume of unlabeled data. We argue that there is still significant room for improvement in the effective utilization of unlabeled data. To this end, we introduce a Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection (SCOUT). It includes an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM). The ADSA module selects valuable data for annotation through an adversarial augment and sampling strategy. The TFM module further leverages the selected valuable data by combining camouflage-related knowledge and text-visual interaction. To adapt to this work, we build a new dataset, namely RefTextCOD. Extensive experiments show that the proposed method surpasses previous semi-supervised methods in the COD field and achieves state-of-the-art performance. Our code will be released at https://github.com/Heartfirey/UCOD-DPL.

In indoor scenes, the diverse distribution of object locations and scales makes the visual 3D perception task a big challenge. Previous works (e.g., NeRF-Det) have demonstrated that implicit representation has the capacity to benefit the visual 3D perception task in indoor scenes with high amount of overlap between input images. However, previous works cannot fully utilize the advancement of implicit representation because of fixed sampling and simple multi-view feature fusion. In this paper, inspired by sparse fashion method (e.g., DETR3D), we propose a simple yet effective method, NeRF-DetS, to address above issues. NeRF-DetS includes two modules: Progressive Adaptive Sampling Strategy (PASS) and Depth-Guided Simplified Multi-Head Attention Fusion (DS-MHA). Specifically, (1) PASS can automatically sample features of each layer within a dense 3D detector, using offsets predicted by the previous layer. (2) DS-MHA can not only efficiently fuse multi-view features with strong occlusion awareness but also reduce computational cost. Extensive experiments on ScanNetV2 dataset demonstrate our NeRF-DetS outperforms NeRF-Det, by achieving +5.02% and +5.92% improvement in mAP under IoU25 and IoU50, respectively. Also, NeRF-DetS shows consistent improvements on ARKITScenes.

Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled fast and high-quality scene reconstruction, research has also explored incorporating open-vocabulary understanding into 3DGS. However, most existing methods require iterative optimization over per-view 2D semantic feature maps, which not only results in inefficiencies but also leads to inconsistent 3D semantics across views. To address these limitations, we introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives. The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities and providing a structured foundation for open-vocabulary understanding. Based on the graph structure, we design an efficient reprojection strategy that lifts 2D semantic features onto the superpoints, avoiding costly multi-view iterative training. The resulting representation ensures strong 3D semantic coherence and naturally supports hierarchical understanding, enabling both coarse- and fine-grained open-vocabulary perception within a unified semantic field. Extensive experiments demonstrate that our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over 30× faster.

Despite growing interest in hallucination in Multimodal Large Language Models, existing studies primarily focus on single-image settings, leaving hallucination in multi-image scenarios largely unexplored. To address this gap, we conduct the first systematic study of hallucinations in multi-image MLLMs and propose MIHBench, a benchmark specifically tailored for evaluating object-related hallucinations across multiple images. MIHBench comprises three core tasks: Multi-Image Object Existence Hallucination, Multi-Image Object Count Hallucination, and Object Identity Consistency Hallucination, targeting semantic understanding across object existence, quantity reasoning, and cross-view identity consistency. Through extensive evaluation, we identify key factors associated with the occurrence of multi-image hallucinations, including: a progressive relationship between the number of image inputs and the likelihood of hallucination occurrences; a strong correlation between single-image hallucination tendencies and those observed in multi-image contexts; and the influence of same-object image ratios and the positional placement of negative samples within image sequences on the occurrence of object identity consistency hallucination. To address these challenges, we propose a Dynamic Attention Balancing mechanism that adjusts inter-image attention distributions while preserving the overall visual attention proportion. Experiments across multiple state-of-the-art MLLMs demonstrate that our method effectively reduces hallucination occurrences and enhances semantic integration and reasoning stability in multi-image scenarios.

Open vocabulary image segmentation tackles the challenge of recognizing dynamically adjustable, predefined novel categories at inference time by leveraging vision-language alignment. However, existing paradigms typically perform class-agnostic region segmentation followed by category matching, which deviates from the human visual system's process of recognizing objects based on semantic concepts, leading to poor alignment between region segmentation and object concepts. To bridge this gap, we propose a novel Cognition-Inspired Framework for open vocabulary image segmentation that emulates the human visual recognition process: first forming a conceptual understanding of an object, then perceiving its spatial extent. The framework consists of three core components: (1) A Generative Vision-Language Model (G-VLM) that mimics human cognition by generating object concepts to provide semantic guidance for region segmentation. (2) A Concept-Aware Visual Enhancer module that fuses textual concept features with global visual representations, enabling adaptive visual perception based on object concepts. (3) A Cognition-Inspired Mask Decoder that integrates local instance features with G-VLM-provided semantic cues, allowing selective classification over a subset of relevant categories. Extensive experiments demonstrate that our framework achieves significant improvements, reaching 27.2 PQ, 17.0 mAP, and 35.3 mIoU on A-150. It further attains 56.2, 28.2, 15.4, 59.2, 18.7, and 95.8 mIoU on Cityscapes, Mapillary Vistas, A-847, PC-59, PC-459, and PAS-20, respectively. In addition, our framework supports vocabulary-free image segmentation, offering enhanced flexibility in recognizing unseen categories.

The reasoning segmentation task involves segmenting objects within an image by interpreting implicit user instructions. Despite significant advancements made by existing approaches, they remain constrained by low perceptual resolution, as visual encoders are typically pre-trained at lower resolutions. Furthermore, simply interpolating the positional embeddings of visual encoders to enhance perceptual resolution yields only marginal performance improvements while incurring substantial computational costs. To address this, we propose HRSeg, an efficient model with high-resolution fine-grained perception. It features two key innovations: High-Resolution Perception (HRP) and High-Resolution Enhancement (HRE). The HRP module processes high-resolution images through cropping, integrating local and global features for multi-granularity quality. The HRE module enhances mask features by integrating fine-grained information from high-resolution images, refining their alignment with text features for precise segmentation. Extensive ablation studies validate the effectiveness of our modules, while comprehensive experiments on multiple benchmark datasets demonstrate HRSeg's superior performance. Code will be available at https://github.com/WeihuangLin/HRSeg.

Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as localization and classification. While several anomaly synthesis approaches have been introduced for data augmentation, they often struggle with low realism, inaccurate mask alignment, and poor generalization. To overcome these limitations, we propose Generate Aligned Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework. GAA leverages the strong priors of a pretrained latent diffusion model to generate realistic, diverse, and semantically aligned anomalies using only a small number of samples. The framework first employs Localized Concept Decomposition to jointly model the semantic features and spatial information of anomalies, enabling flexible control over the type and location of anomalies. It then utilizes Adaptive Multi-Round Anomaly Clustering to perform fine-grained semantic clustering of anomaly concepts, thereby enhancing the consistency of anomaly representations. Subsequently, a region-guided mask generation strategy ensures precise alignment between anomalies and their corresponding masks, while a low-quality sample filtering module is introduced to further improve the overall quality of the generated samples. Extensive experiments on the MVTec AD and LOCO datasets demonstrate that GAA achieves superior performance in both anomaly synthesis quality and downstream tasks such as localization and classification.

Text-based Person Retrieval (TBPR) is a challenging task that aims to retrieve pedestrian images according to natural language descriptions. Existing works mainly focus on discriminative feature learning via exploring cross-modal matching methods, while the overfitting issues caused by insufficient labeled data and the absence of well-designed auxiliary tasks are often overlooked. Motivated by the recent progress of large language models (LLMs), we propose a novel method named GPT-ReID for TBPR, which aims to leverage the strong comprehension of LLMs to alleviate the overfitting risk. Specifically, based on the great power of GPT, GPT-ReID first introduces an adversarial text generation scheme called GPTGAN, which aims to generate comprehensive strong positive captions and deceptive hard negative captions through the original captions for a single image. Furthermore, a joint auxiliary learning strategy is also proposed which contains Multi-Relation Aware (MRA), Keywords Masked Language Model (KMLM), and Keywords Replacement Detection (KRD), to facilitate global- and token-level optimization, enhancing cross-modal granular representation alignment. Extensive experiments on a set of highly competitive benchmark datasets validate the merits of the proposed GPT-ReID against a flurry of state-of-the-art methods, with Rank-1 accuracy reaching 78.42%, 69.43%, and 70.06% on CUHK-PEDS, ICFG-PEDS, and RSTPReid, respectively.

Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models but face prohibitive memory demands due to massive parameterization. Existing pruning methods rely on heuristic metrics or impractical enumeration of expert subsets, leading to suboptimal performance or scalability. In this paper, we propose Shapley-MoE, an efficient pruning method for MoE models inspired by cooperative game theory. By quantifying each expert’s contribution via Shapley value, our method identifies important experts without exhaustive combination evaluations. To overcome the NP-hard complexity of exact Shapley computation, we introduce a Monte Carlo sampling strategy for efficient approximation that reduces complexity to quadratic time. However, vanilla Monte Carlo sampling still faces issues of insufficient estimation accuracy and low sampling efficiency. To address these issues, we further propose two novel methods to improve sampling accuracy and efficiency: (1) Early Truncation, which early terminates unstable sampling steps caused by overly small expert subsets, and (2) Router-Guided Importance Sampling, which prioritize sampling important expert subsets using gating activation probabilities. Both theoretical and experimental analyses show that both methods can accelerate Shapley value estimation and improve accuracy. Extensive empirical evaluations demonstrate that our pruned MoE models outperform existing expert pruning methods. Notably, when applied to the Qwen2-57B-A14B model, our method reduces the number of experts by 25% with only a 0.92 increase in perplexity and over 96.4% of the average zero-shot accuracy is maintained.

Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels as supervision. Despite recent advancements incorporating transformers into WSOL have resulted in improvements, these methods often rely on category-agnostic attention maps, leading to suboptimal object localization. This paper presents a novel CLIP-Driven TRansformer (CDTR) that learns category-aware representations for accurate object localization. Specifically, we initially propose a Category-aware Stimulation Module (CSM) that embeds learnable category biases into self-attention maps, enhancing the learning process with auxiliary supervision. Additionally, an Object Constraint Module (OCM) is designed to refine object regions in a self-supervised manner, leveraging the discriminative potential of the self-attention maps provided by CSM. To create a synergistic connection between CSM and OCM, we further develop a Semantic Kernel Integrator (SKI), which generates a semantic kernel for self-attention maps. Meanwhile, we explore the CLIP model and design a Semantic Boost Adapter (SBA) to enrich object representations by integrating semantic-specific image and text representations into self-attention maps. Extensive experimental evaluations on benchmark datasets, such as CUB-200-2011 and ILSVRC highlight the superior performance of our CDTR framework.

In this paper, we present UISE, a unified image segmentation framework that achieves efficient performance across various segmentation tasks, eliminating the need for multiple specialized pipelines. UISE employs dynamic convolutions between universal segmentation kernels and image feature maps, enabling a single pipeline for different tasks such as panoptic, instance, semantic, and video instance segmentation. To address computational requirements, we introduce a feature pyramid aggregator for image feature extraction and a separable dynamic decoder for generating segmentation kernels. The aggregator re-parameterizes interpolation-first modules in a convolution-first manner, resulting in a significant acceleration of the pipeline without incurring additional costs. The decoder incorporates multi-head cross-attention through separable dynamic convolution, enhancing both efficiency and accuracy. Extensive experiments are conducted to validate UISE's performance across different segmentation tasks. To the best of our knowledge, UISE is the first universal segmentation framework that delivers competitive performance in terms of both speed and accuracy when compared to current state-of-the-art models.

The focus of Face Anti-Spoofing (FAS) is shifting toward improving generalization performance in unseen scenarios. Traditional methods employing adversarial learning and meta-learning aim to extract or decouple generalizable features to address these challenges. However, enhancing performance solely through facial features remains challenging without additional informative inputs. To address this, Vision-Language Models (VLMs) with robust generalization capabilities have recently been introduced to FAS. Despite their potential, these VLMs typically adopt a late alignment strategy, relying only on encoder output features for modality alignment, which largely neglects mutual guidance between modalities. To bridge this gap, inspired by recent advancements in prompt learning, we employ learnable prompts and masking as intermediaries to enhance interaction between text and visual modalities, enabling the extraction of more generalizable features. Specifically, we propose ME-FAS, a Modality-Enhanced cross-domain FAS model integrating Prompt Fusion Transfer (PFT) and Text-guided Image Masking (TIM). PFT facilitates the integration of text features with visual information, improving domain adaptability in alignment with the textual context. Meanwhile, TIM leverages text features to mask image patches, directing visual features toward critical generalizable facial information, such as the eyes and mouth. Comprehensive evaluations across multiple benchmarks and various visualizations demonstrate significant performance gains, validating the effectiveness of our proposed approach. Our code and models are available at https://github.com/clpbc/ME-FAS.

Weakly supervised object localization (WSOL) stands as a pivotal endeavor within the realm of computer vision, entailing the location of objects utilizing merely image-level labels. Contemporary approaches in WSOL have leveraged FPMs, yielding commendable outcomes. However, these existing FPM-based techniques are predominantly confined to rudimentary strategies of either augmenting the foreground or diminishing the background presence. We argue for the exploration and exploitation of the intricate interplay between the object's foreground and its background to achieve efficient object localization. In this manuscript, we introduce an innovative framework, termed adaptive zone learning (AZL), which operates on a coarse-to-fine basis to refine FPMs through a triad of adaptive zone mechanisms. First, an adversarial learning mechanism (ALM) is employed, orchestrating an interplay between the foreground and background regions. This mechanism accentuates coarse-grained object regions in a mutually adversarial manner. Subsequently, an oriented learning mechanism (OLM) is unveiled, which harnesses local insights from both foreground and background in a fine-grained manner. This mechanism is instrumental in delineating object regions with greater granularity, thereby generating better FPMs. Furthermore, we propose a reinforced learning mechanism (RLM) as the compensatory mechanism for adversarial design, by which the undesirable foreground maps are refined again. Extensive experiments on CUB-200-2011 and ILSVRC datasets demonstrate that AZL achieves significant and consistent performance improvements over other state-of-the-art WSOL methods.

Unsupervised domain adaptation (UDA) person reidentification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This article proposes a dual-level asymmetric mutual learning (DAML) method to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning (AML) manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts (SOTA).

Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset. In this paper, we propose a novel weakly supervised open-vocabulary object detection framework, namely WSOVOD, to extend traditional WSOD to detect novel concepts and utilize diverse datasets with only image-level annotations. To achieve this, we explore three vital strategies, including dataset-level feature adaptation, image-level salient object localization, and region-level vision-language alignment. First, we perform data-aware feature extraction to produce an input-conditional coefficient, which is leveraged into dataset attribute prototypes to identify dataset bias and help achieve cross-dataset generalization. Second, a customized location-oriented weakly supervised region proposal network is proposed to utilize high-level semantic layouts from the category-agnostic segment anything model to distinguish object boundaries. Lastly, we introduce a proposal-concept synchronized multiple-instance network, i.e., object mining and refinement with visual-semantic alignment, to discover objects matched to the text embeddings of concepts. Extensive experiments on Pascal VOC and MS COCO demonstrate that the proposed WSOVOD achieves new state-of-the-art compared with previous WSOD methods in both close-set object localization and detection tasks. Meanwhile, WSOVOD enables cross-dataset and open-vocabulary learning to achieve on-par or even better performance than well-established fully-supervised open-vocabulary object detection (FSOVOD).

While generic person re-identification has made remarkable improvement in recent years, these methods are designed under the assumption that the entire body of the person is available. This assumption brings about a significant performance degradation when suffering from occlusion caused by various obstacles in real-world applications. To address this issue, data-driven strategies have emerged to enhance the model's robustness to occlusion. Following the random erasing paradigm, these strategies typically employ randomly generated noise to supersede randomly selected image regions to simulate obstacles. However, the random strategy is not sensitive to location and content, meaning they cannot mimic real-world occlusion cases in application scenarios. To overcome this limitation and fully exploit the real scene information in datasets, this paper proposes a more intuitive and effective data-driven strategy named Saliency-Guided Patch Transfer (SPT). Combined with the vision transformer, SPT divides person instances and background obstacles using salient patch selection. By transferring person instances to different background obstacles, SPT can easily generate photo-realistic occluded samples. Furthermore, we propose an occlusion-aware Intersection over Union (OIoU) with mask-rolling to filter the more suitable combination and a class-ignoring strategy to achieve more stable processing. Extensive experimental evaluations conducted on occluded and holistic person re-identification benchmarks demonstrate that SPT provides a significant performance gain among different ViT-based ReID algorithms on occluded ReID.

Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a DualPath Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dualpath interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.

The simulated annealing algorithm aims to improve model convergence through multiple restarts of training. However, existing annealing algorithms overlook the cor-relation between different cycles, neglecting the potential for incremental learning. We contend that a fixed network structure prevents the model from recognizing distinct features at different training stages. To this end, we propose RepAn, redesigning the irreversible re-parameterization (Rep) method and integrating it with annealing to enhance training. Specifically, the network goes through Rep, ex-pansion, restoration, and backpropagation operations during training, and iterating through these processes in each annealing round. Such a method exhibits good generalization and is easy to apply, and we provide theoretical expla-nations for its effectiveness. Experiments demonstrate that our method improves baseline performance by 6.38% on the CIFAR-100 dataset and 2.80% on ImageNet, achieving state-of-the-art performance in the Rep field. The code is available at https://github.com/xfey/RepAn.

The Segment Anything Model (SAM) marks a notable milestone in segmentation models, highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightweight decoder, ensuring efficient real-time performance. However, SAM faces stability issues in challenging samples upon this pipeline. These issues arise from two main factors. Firstly, the image preprocessing disables SAM to dynamically use image-level zoom-in strategies to refocus on the target object during interaction. Secondly, the lightweight decoder struggles to sufficiently integrate interactive information with image embeddings. To address these two limitations, we propose FocSAM with a pipeline redesigned on two pivotal aspects. First, we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Dwin-MSA localizes attention computations around the target object, enhancing object-related embeddings with minimal computational overhead. Second, we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks that have significant impacts on the overall segmentation results. Experimentally, FocSAM augments SAM's interactive segmentation performance to match the existing state-of-the-art method in segmentation quality, requiring only about 5.6% of this method's inference time on CPUs. Code is available at https://github.com/YouHuang67/focsam.

Post-training Sparsity (PTS) is a recently emerged av-enue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset, especially at high sparsity ratios. In this paper, we attempt to reconcile this disparity by transposing three cardinal factors that profoundly alter the performance of conventional sparsity into the context of PTS. Our endeav-ors particularly comprise (1) A base-decayed sparsity objective that promotes efficient knowledge transferring from dense network to the sparse counterpart. (2) A reducing-regrowing search algorithm designed to ascertain the op-timal sparsity distribution while circumventing overfitting to the small calibration set in PTS. (3) The employment of dynamic sparse training predicated on the preceding as-pects, aimed at comprehensively optimizing the sparsity structure while ensuring training stability. Our proposed framework, termed UniPTS, is validated to be much superior to existing PTS methods across extensive benchmarks. As an illustration, it amplifies the performance of POT, a recently proposed recipe, from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet. We re-lease the code of our paper at https://github.com/xjjxmu/UniPTS.

With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, the visual reasoning problem is usually decomposed into manageable sub-tasks and tackled sequentially with various external tools. However, such a paradigm faces the challenge of the potential "determining hallucinations" in decision generation due to insufficient visual information and the limitation of low-level perception tools that fail to provide abstract summaries necessary for comprehensive reasoning. We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. This paper delves into the realm of multimodal CoT to solve intricate visual reasoning tasks with multimodal large language models(MLLMs) and their cognitive capability. To this end, we propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Cantor first acts as a decision generator and integrates visual inputs to analyze the image and problem, ensuring a closer alignment with the actual context. Furthermore, Cantor leverages the advanced cognitive functions of MLLMs to perform as multifaceted experts for deriving higher-level information, enhancing the CoT generation process. Our extensive experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance across two complex visual reasoning datasets, without necessitating fine-tuning or ground-truth rationales. Project Page: https://ggg0919.github.io/cantor/.

Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional "training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. This significantly limits the models' generalization capabilities and efficiency in deployment. In this study, we propose a novel adaptation paradigm, termed "prompting-to-adapt'', to tackle the above issue by introducing an innovative image prompter. This prompter generates domain-specific prompts through few-shot image-mask pairs, incorporating diverse image processing techniques to enhance adaptability. To tackle the inherent non-differentiability of image prompts, we further devise an information-estimation-based gradient descent strategy that leverages the information entropy of image processing combinations to optimize the prompter, ensuring effective adaptation. Through extensive experiments across nine datasets spanning seven image domains (i.e., depth, thermal, camouflage, endoscopic, ultrasound, grayscale, and natural) and four scenarios (i.e., common scenes, camouflage objects, medical images, and industrial data), we demonstrate that our approach significant improves the foundational models' adaptation capabilities. Moreover, the interpretability of the generated prompts provides insightful revelations into their image processing mechanisms. Source code is available at: \urlgithub.com/yuema1303/Prompting-to-Adapt-FSM.

This paper presents the first study to explore the potential of parameter quantization for multimodal large language models to alleviate the significant resource constraint encountered during vision-language instruction tuning. We introduce a Quantization-aware Scale LeArning method based on multimodal Warmup, termed QSLAW. This method is grounded in two key innovations: (1) The learning of group-wise scale factors for quantized LLM weights to mitigate the quantization error arising from activation outliers and achieve more effective vision-language instruction tuning; (2) The implementation of a multimodal warmup that progressively integrates linguistic and multimodal training samples, thereby preventing overfitting of the quantized model to multimodal data while ensuring stable adaptation of multimodal large language models to downstream vision-language tasks. Extensive experiments demonstrate that models quantized by QSLAW perform on par with, or even surpass, their full-precision counterparts, while facilitating up to 1.4 times reduction in VL tuning time and GPU consumption.

Referring image segmentation (RIS) aims to segment a particular region based on a specific expression. Existing one-stage methods have explored various fusion strategies, yet they encounter two significant issues. Primarily, most methods rely on manually selected visual features from the visual encoder layers. Moreover, the direct fusion of word-level features into coarse aligned features disrupts the established vision-language alignment. In this paper, we introduce an innovative framework for RIS that seeks to overcome these challenges with adaptive alignment of vision and language features, termed the Adaptive Selection with Dual Alignment (ASDA). ASDA innovates in two aspects. Firstly, we design an Adaptive Feature Selection and Fusion (AFSF) module to dynamically select visual features focusing on different regions related to various descriptions. AFSF is equipped with scale-wise feature aggregator to provide hierarchically coarse features that preserve crucial low-level details. Secondly, a Word Guided Dual-Branch Aligner (WGDA) is leveraged to integrate coarse features with linguistic cues by word-guided attention, which effectively addresses the common issue of vision-language misalignment. Extensive experimental results demonstrate that our ASDA framework surpasses state-of-the-art methods on RefCOCO, RefCOCO+ and G-Ref benchmark.

Recent advancements in 3D generation have leveraged synthetic datasets with ground truth 3D assets and predefined camera trajectories. However, the potential of adopting real-world datasets, which can produce significantly more realistic 3D scenes, remains largely unexplored. In this work, we delve into the key challenge of the complex and scene-specific camera trajectories found in real-world captures. We introduce Director3D, a robust open-world text-to-3D generation framework, designed to generate both real-world 3D scenes and adaptive camera trajectories. To achieve this, (1) we first utilize a Trajectory Diffusion Transformer, acting as the \emph{Cinematographer}, to model the distribution of camera trajectories based on textual descriptions. Next, a Gaussian-driven Multi-view Latent Diffusion Model serves as the \emph{Decorator}, modeling the image sequence distribution given the camera trajectories and texts. This model, fine-tuned from a 2D diffusion model, directly generates pixel-aligned 3D Gaussians as an immediate 3D scene representation for consistent denoising. Lastly, the 3D Gaussians are further refined by a novel SDS++ loss as the \emph{Detailer}, which incorporates the prior of the 2D diffusion model. Extensive experiments demonstrate that Director3D outperforms existing methods, offering superior performance in real-world 3D generation.

Transformer-based instance-level recognition has attracted increasing research attention recently due to the superior performance. However, although attempts have been made to encode masks as embeddings into Transformer-based frameworks, how to combine mask embeddings and spatial information for a transformer-based approach is still not fully explored. In this paper, we revisit the design of mask-embedding-based pipelines and propose an Instance Segmentation TRansformer (ISTR) with Mask Meta-Embeddings (MME), leveraging the strengths of transformer models in encoding embedding information and incorporating spatial information from mask embeddings. ISTR incorporates a recurrent refining head that consists of a Dynamic Box Predictor (DBP), a Mask Information Generator (MIG), and a Mask Meta-Decoder (MMD). To improve the quality of mask embeddings, MME interprets the mask encoding-decoding processes as a mutual information maximization problem, which unifies the objective functions of different decoding schemes such as Principal Component Analysis (PCA) and Discrete Cosine Transform (DCT) with a meta-formulation. Under the meta-formulation, a learnable Spatial Mask Tuner (SMT) is further proposed, which fuses the spatial and embedding information produced from MIG and can significantly boost the segmentation performance. The resulting varieties, i.e., ISTR-PCA, ISTR-DCT, and ISTR-SMT, demonstrate the effectiveness and efficiency of incorporating mask embeddings with the query-based instance segmentation pipelines. On the COCO dataset, ISTR surpasses all predominant mask-embedding-based models by a large margin, and achieves competitive performance compared to concurrent state-of-the-art models. On the Cityscapes dataset, ISTR also outperforms several strong baselines. Our code has been made available at: https://github.com/hujiecpp/ISTR.

Referring image segmentation aims to segment objects that are described by natural language expressions. Although remarkable advancements have been made to align natural language expressions with visual representations for better performance, the interaction between image-level and text-level information is still not formulated properly. Most of the previous works focus on building correlations between vision and language, ignoring the variety of objects. The target objects with unique appearances may not be correctly located or completely segmented. In this article, we propose a novel Bilateral Knowledge Interaction Network, termed BKINet, which reformulates the image-text interaction in a bilateral manner to adapt concrete knowledge of the target object in the image. BKINet contains two key components: a knowledge learning module (KLM) and a knowledge applying module (KAM). In the KLM, the abstract knowledge from text features is replenished with concrete knowledge from visual features to adapt to the target objects in the input images, which generates the knowledge interaction kernels (KI kernels) containing abundant referring information. With the referring information of KI kernels, the KAM is designed to highlight the most relevant visual features for predicting the accurate segmentation mask. Extensive experiments on three widely-used datasets,i.e.RefCOCO, RefCOCO+, and G-ref, demonstrate the superiority of BKINet over the state-of-the-art.

In shilling attacks, an adversarial party injects a few fake user profiles into a Recommender System (RS) so that the target item can be promoted or demoted. Although much effort has been devoted to developing shilling attack methods, we find that existing approaches are still far from practical. In this paper, we analyze the properties a practical shilling attack method should have and propose a new concept of Cross-system Attack. With the idea of Cross-system Attack, we design a Practical Cross-system Shilling Attack (PC-Attack) framework that requires little information about the victim RS model and the target RS data for conducting attacks. PC-Attack is trained to capture graph topology knowledge from public RS data in a self-supervised manner. Then, it is fine-tuned on a small portion of target data that is easy to access to construct fake profiles. Extensive experiments have demonstrated the superiority of PC-Attack over state-of-the-art baselines. Our implementation of PC-Attack is available at https://github.com/KDEGroup/PC-Attack.

The real-world Facial Expression Recognition (FER) datasets usually exhibit complex scenarios with coupled noise annotations and imbalanced classes distribution, which undoubtedly impede the development of FER methods. To address the aforementioned issues, in this paper, we propose a novel and flexible method to spot noisy labels by leveraging adversarial attack, termed as Geometry Aware Adversarial Vulnerability Estimation (GAAVE). Different from existing state-of-the-art methods of noisy label learning (NLL), our method has no reliance on additional information and is thus easy to generalize to the large-scale real-world FER datasets. Besides, the combination of Dataset Splitting module and Subset Refactoring module mitigates the impact of class imbalance, and the Self-Annotator module facilitates the sufficient use of all training data. Extensive experiments on RAF-DB, FERPlus, AffectNet, and CIFAR-10 datasets validate the effectiveness of our method. The stabilized enhancement based on different methods demonstrates the flexibility of our proposed GAAVE.

In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dynamic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a feature pyramid aggregator for the feature map extraction, and a separable dynamic decoder for the panoptic kernel generation. The aggregator re-parameterizes interpolation-first modules in a convolution-first way, which significantly speeds up the pipeline without any additional costs. The decoder performs multi-head cross-attention via separable dynamic convolution for better efficiency and accuracy. To the best of our knowledge, YOSO is the first real-time panoptic segmentation framework that delivers competitive performance compared to state-of-the-art models. Specifically, YOSO achieves 46.4 PQ, 45.6 FPS on COCO; 52.5 PQ, 22.6 FPS on Cityscapes; 38.0 PQ, 35.4 FPS on ADE20K; and 34.1 PQ, 7.1 FPS on Mapillary Vistas. Code is available at https://github.com/hujiecpp/YOSO.

In the field of human pose estimation, regression-based methods have been dominated in terms of speed, while heatmap-based methods are far ahead in terms of performance. How to take advantage of both schemes remains a challenging problem. In this paper, we propose a novel human pose estimation framework termed DistilPose, which bridges the gaps between heatmap-based and regression-based methods. Specifically, DistilPose maximizes the transfer of knowledge from the teacher model (heatmap-based) to the student model (regression-based) through Token-distilling Encoder (TDE) and Simulated Heatmaps. TDE aligns the feature spaces of heatmap-based and regression-based models by introducing tokenization, while Simulated Heatmaps transfer explicit guidance (distribution and confidence) from teacher heatmaps into student models. Extensive experiments show that the proposed DistilPose can significantly improve the performance of the regression-based models while maintaining efficiency. Specifically, on the MSCOCO validation dataset, DistilPose-S obtains 71.6% mAP with 5.36M parameters, 2.38 GFLOPs, and 40.2 FPS, which saves 12.95×, 7.16× computational cost and is 4.9× faster than its teacher model with only 0.9 points performance drop. Furthermore, DistilPose-L obtains 74.4% mAP on MSCOCO validation dataset, achieving a new state-of-the-art among predominant regression-based models. Code will be available at https://github.com/yshMars/DistilPose.

With the widespread deployment of face authentication systems, domain generalization (DG) based face anti-spoofing (FAS) security approaches have drawn growing attention. Existing generalization-based methods always attempt to extract domain-invariant task information from data and eliminate domain-dependent information from representation space. However, they neglect that domain-related information may also contain helpful features for the classification task. To address this issue, we propose a self-paced partial domain-aware framework (SPDA) to preserve domain-related features helpful for the discrimination of fake and real faces, thereby increasing generalization for unseen domains. Specifically, a training strategy based on contrastive learning is adopted to construct domain-adapted and domain-aware task-related representation spaces. Then, a partial domain-aware adaptation module (PDA) is proposed to preserve valuable domain-related information for the task features that the network considers useful for mixture-domain classification. In addition, the proposed self-paced method(SCM) continuously explores potential clusters with insufficient representation to enhance further the feature extractor’s capability and the effectiveness of the PDA module. Extensive experiments demonstrate the effectiveness of our method compared to SOTA algorithms.

Instance segmentation has been dominated by the paradigm that predicts masks using local RoI features and simplicity frameworks based on global mask prediction. Despite the comparable performance between local-based and global-based approaches, the AP results of objects on different scales vary significantly. In this paper, we first point out that the key factor to bridging such a gap lies in the utilization of local RoI information for global mask prediction. Then, we observe a ’class-agnostic segmentation’ problem exists in the nearby region of interesting objects after implementing the above combination. To overcome this issue, we further propose a CAtegory-kerNelized DYnamic (CANDY) convolution. Benefiting from it, the discriminative ability of the resulting instance segmentation framework, i.e., CANDY-Mask, on foreground objects is significantly enhanced. Extensive experiments on the MS-COCO dataset are conducted to verify the performance of CANDY-Mask. Our proposed CANDY-Mask obtains 48.1% boxes AP and 40.7% masks AP on the MS-COCO test-dev set with ResNet50 backbone, achieving state-of-the-art among various models.

Weakly supervised object localization (WSOL) aims to localize objects based on only image-level labels as supervision. Recently, transformers have been introduced into WSOL, yielding impressive results. The self-attention mechanism and multilayer perceptron structure in transformers preserve long-range feature dependency, facilitating complete localization of the full object extent. However, current transformer-based methods predict bounding boxes using category-agnostic attention maps, which may lead to confused and noisy object localization. To address this issue, we propose a novel Category-aware Allocation TRansformer (CATR) that learns category-aware representations for specific objects and produces corresponding category-aware attention maps for object localization. First, we introduce a Category-aware Stimulation Module (CSM) to induce learnable category biases for self-attention maps, providing auxiliary supervision to guide the learning of more effective transformer representations. Second, we design an Object Constraint Module (OCM) to refine the object regions for the category-aware attention maps in a self-supervised manner. Extensive experiments on the CUB-200-2011 and ILSVRC datasets demonstrate that the proposed CATR achieves significant and consistent performance improvements over competing approaches.

Pseudo-labeling is significant for semi-supervised instance segmentation, which generates instance masks and classes from unannotated images for subsequent training. However, in existing pipelines, pseudo-labels that contain valuable information may be directly filtered out due to mismatches in class and mask quality. To address this issue, we propose a novel framework, called pseudo-label aligning instance segmentation (PAIS), in this paper. In PAIS, we devise a dynamic aligning loss (DALoss) that adjusts the weights of semi-supervised loss terms with varying class and mask score pairs. Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited. Notably, with just 1% labeled data, PAIS achieves 21.2 mAP (based on MaskRCNN) and 19.9 mAP (based on K-Net) on the COCO dataset, outperforming the current state-of-the-art model, i.e., NoisyBoundary with 7.7 mAP, by a margin of over 12 points. Code is available at: https://github.com/hujiecpp/PAIS.

Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators’ later click is based on models’ feedback of annotators’ former click. This serial interaction is unable to utilize model’s parallelism capabilities. Second, in each interaction step, the model handles the invariant image along with the sparse variable clicks, resulting in a process that’s highly repetitive and redundant. For efficient computations, we propose a method named InterFormer that follows a new pipeline to address these issues. In-terFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to prepro-cess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. Furthermore, the I-MSA module’s deployment on low-power devices extends the practical application of interactive segmentation. The I-MSA module utilizes the preprocessed features to efficiently response to the annotator inputs in real-time. The experiments on several datasets demonstrate the effectiveness of Inter-Former, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices. The code is available at https://github.com/YouHuang67/InterFormer.

A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource-adaptive supernet, termed PSS-Net. We maintain multiple subnet pools, each of which stores the information of substantial subnets with similar resource consumption. Considering a resource constraint, subnets conditioned on this resource constraint are sampled from a pre-defined subnet structure space and high-quality ones will be inserted into the corresponding subnet pool. Then, the sampling will gradually be prone to sampling subnets from the subnet pools. Moreover, the one with a better performance metric is assigned with higher priority to train our PSS-Net, if sampling is from a subnet pool. At the end of training, our PSS-Net retains the best subnet in each pool to entitle a fast switch of high-quality subnets for inference when the available resources vary. Experiments on ImageNet using MobileNet-V1/V2 and ResNet-50 show that our PSS-Net can well outperform state-of-the-art resource-adaptive supernets. Our project is publicly available at https://github.com/chenbong/PSS-Net.

Self-attention (SA) based networks have achieved great success in image captioning, constantly dominating the leaderboards of online benchmarks. However, existing SA networks still suffer from distance insensitivity and low-rank bottleneck. In this paper, we aim to optimize SA in terms of two aspects, thereby addressing the above issues. First, we introduce a Distance-sensitive Self-Attention (DSA), which considers the raw geometric distances between query-key pairs in the 2D images during SA modeling. Second, we present a simple yet effective approach, named Multi-branch Self-Attention (MSA) to compensate for the low-rank bottleneck. MSA treats a multi-head self-attention layer as a branch and duplicates it multiple times to increase the expressive power of SA. To validate the effectiveness of the two designs, we apply them to the standard self-attention network, and conduct extensive experiments on the highly competitive MS-COCO dataset. We achieve new state-of-the-art performance on both the local and online test sets,i.e., 135.1% CIDEr on the Karpathy split and 135.4% CIDEr on the official online split.

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a k -reciprocal nearest filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are nonlearning processes, which, thus, significantly reduces the pruning complexity and differentiates our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-five accuracy drops. Our project is available at https://github.com/lmbxmu/CLR-RNF.

Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.

Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, GuidedMix-Net employs three operations: 1) interpolation of similar labeled-unlabeled image pairs; 2) transfer of mutual information; 3) generalization of pseudo masks. It enables segmentation models can learning the higher-quality pseudo masks of unlabeled data by transfer the knowledge from labeled samples to unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU over 7$\%$ compared to previous approaches.

In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher11Source code are available at: https://github.com/HunterJLin/ActiveTeacher for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, i.e., Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, i.e. 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications.

GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability, i.e. reconstructing the input image accurately and editing the inverted image with a small visual quality drop. The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code. Here, we show that both reconstruction and editability can be improved by a proper design of the pivot code. We present a simple yet effective method, named cycle encoding, for a high-quality pivot code. The key idea of our method is to progressively train an encoder in varying spaces according to a cycle scheme: W->W+->W. This training methodology preserves the properties of both W and W+ spaces, i.e. high editability of W and low distortion of W+. To further decrease the distortion, we also propose to refine the pivot code with an optimization-based method, where a regularization term is introduced to reduce the degradation in editability. Qualitative and quantitative comparisons to several state-of-the-art methods demonstrate the superiority of our approach.

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e., Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this issue, we introduce Group-wise Transformation towards a universal yet lightweight Transformer for vision-and-language tasks, termed as LW-Transformer. LW-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN. We apply LW-Transformer to a set of Transformer-based networks, and quantitatively measure them on three vision-and-language tasks and six benchmark datasets. Experimental results show that while saving a large number of parameters and computations, LW-Transformer achieves very competitive performance against the original Transformer networks for vision-and-language tasks. To examine the generalization ability, we apply LW-Transformer to the task of image classification, and build its network based on a recently proposed image Transformer called Swin-Transformer, where the effectiveness can be also confirmed.

Text-to-Image (T2I) synthesis is a challenging task that aims to convert natural language descriptions to real images. It remains an open problem mainly due to the diversity of text descriptions, which poses a huge obstacle in generating vivid and relevant images. Moreover, the existing evaluation metrics in T2I synthesis are mainly used to evaluate the visual quality of the generated images, while the semantic consistency between the two modalities is often ignored. To address these issues, we present a novelKnowledge-Driven Generative Adversarial Network, termed KD-GAN, and a new evaluation system, namedPseudo Turing Test(PTT for short). Concretely, KD-GAN takes a further step in imitating the behavior of human painting,i.e., drawing an image according to reference knowledge. The introduction of reference knowledge in KD-GAN not only improves the quality of the generated images but also enhances the semantic consistency between them and the input texts. In addition, KD-GAN can also greatly avoid some flaws against common sense during image generation,e.g., skiing in the blue sky. The proposed PTT is an important supplement to the existing evaluation system of T2I synthesis. It includes a set of pseudo-experts of different multimedia tasks to evaluate the semantic consistency between the given texts and the generated images. To validate the proposed KD-GAN, we conducted extensive experiments on two benchmark datasets,i.e., Caltech-UCSD Birds (CUB), and MS-COCO (COCO). The experimental results demonstrate that KD-GAN outperforms state-of-the-art methods on IS, FID, and the proposed PTT metrics.11The codes of KD-GAN are at [Online]. Available:https://github.com/pengjunn/KD-GANand the codes and models of PTT are at [Online]. Available:https://github.com/pengjunn/PTT.

We propose a novel network pruning approach by information preserving of pretrained network weights (filters). Network pruning with the information preserving is formulated as a matrix sketch problem, which is efficiently solved by the off-the-shelf frequent direction method. Our approach, referred to as FilterSketch, encodes the second-order information of pretrained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure. FilterSketch requires neither training from scratch nor data-driven iterative optimization, leading to a several-orders-of-magnitude reduction of time cost in the optimization of pruning. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of floating-point operations (FLOPs) and prunes 59.9% of network parameters with negligible accuracy cost for ResNet-110. On ILSVRC-2012, it reduces 45.5% of FLOPs and removes 43.0% of parameters with only 0.69% accuracy drop for ResNet-50. Our code and pruned models can be found at https://github.com/lmbxmu/FilterSketch.

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novel Dual-way Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i.e., 133.8% CIDEr on Karpathy split and 135.4% CIDEr on the official split.

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://github.com/imlixinyang/HiSD.

In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the partial information from the intra-class. However, these methods ignore the valuable in-formation from the inter-class, which is for estimating the recognizability of face image. In this work, we argue that a high-quality face image should be similar to its intra-class samples and dissimilar to its inter-class samples. Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by calculating the Wasserstein Distance (WD) between the intra-class and inter-class similarity distributions. With these quality pseudo-labels, we are capable of training a regression network for quality prediction. Extensive experiments on benchmark datasets demonstrate that the proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin. Meanwhile, our method shows good generalization across different recognition systems.

Panoptic segmentation aims to partition an image to object instances and semantic content for thing and stuff categories, respectively. To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored. In this paper, we propose an efficient jointly thing-and-stuff mining (JTSM) framework for WSPS. To this end, we design a novel mask of interest pooling (MoIPool) to extract fixed-size pixel-accurate feature maps of arbitrary-shape segmentations. MoIPool enables a panoptic mining branch to leverage multiple instance learning (MIL) to recognize things and stuff segmentation in a unified manner. We further refine segmentation masks with parallel instance and semantic segmentation branches via self-training, which collaborates the mined masks from panoptic mining with bottom-up object evidence as pseudo-ground-truth labels to improve spatial coherence and contour localization. Experimental results demonstrate the effectiveness of JTSM on PASCAL VOC and MS COCO. As a by-product, we achieve competitive results for weakly supervised object detection and instance segmentation. This work is a first step towards tackling challenge panoptic segmentation task with only image-level labels.

Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies typically involve linking semantic concepts to units or layers of DNNs, but fail to explain the inference process. In this paper, we introduce neural architecture disentanglement (NAD) to fill the gap. Specifically, NAD learns to disentangle a pre-trained DNN into sub-architectures according to independent tasks, forming information flows that describe the inference processes. We investigate whether, where, and how the disentanglement occurs through experiments conducted with handcrafted and automatically-searched network architectures, on both object-based and scene-based datasets. Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs. First, DNNs can be divided into sub-architectures for independent tasks. Second, deeper layers do not always correspond to higher semantics. Third, the connection type in a DNN affects how the information flows across layers, leading to different disentanglement behaviors. With NAD, we further explain why DNNs sometimes give wrong predictions. Experimental results show that misclassified images have a high probability of being assigned to task sub-architectures similar to the correct ones. Our code is available at https://github.com/hujiecpp/NAD.

Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention. To date, bottom-up WSIS methods refine discriminative cues from classifiers with sophisticated multi-stage training procedures, which also suffer from inconsistent object boundaries. And top-down WSIS methods are formulated as cascade detection-to-segmentation pipeline, in which the quality of segmentation learning heavily depends on pseudo masks generated from detectors. In this paper, we propose a unified parallel detection-and-segmentation learning (PDSL) framework to learn instance segmentation with only image-level labels, which draws inspiration from both top-down and bottom-up instance segmentation approaches. The detection module is the same as the typical design of any weakly supervised object detection, while the segmentation module leverages self-supervised learning to model class-agnostic foreground extraction, following by self-training to refine class-specific segmentation. We further design instance-activation correlation module to improve the coherence between detection and segmentation branches. Extensive experiments verify that the proposed method outperforms baselines and achieves the state-of-the-art results on PASCAL VOC and MS COCO.

Based on the relaxed search space, differential architecture search (DARTS) is efficient in searching for a high-performance architecture. However, the unbalanced competition among operations that have different trainable parameters causes the model collapse. Besides, the inconsistent structures in the search and retraining stages causes cross-stage evaluation to be unstable. In this paper, we call these issues as an operation gap and a structure gap in DARTS. To shrink these gaps, we propose to induce equalized and consistent optimization in differentiable architecture search (EC-DARTS). EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked. Besides, we introduce an induced structural transition to bridge the structure gap between the model structures in the search and retraining stages. Extensive experiments on CIFAR10 and ImageNet demonstrate the effectiveness of our method. Specifically, on CIFAR10, we achieve a test error of 2.39%, while only 0.3 GPU days on NVIDIA TITAN V. On ImageNet, our method achieves a top-1 error of 23.6% under the mobile setting.

Weakly supervised object localization (WSOL) has gained recent popularity, which seeks to train localizers with only image-level labels. However, due to relying heavily on classification objective for training, prevailing WSOL methods only localize discriminative parts of object, ignoring other useful information, such as the wings of a bird, and suffer from severe rotation variations. Moreover, learning object localization imposes CNNs to attend non-salient regions under weak supervision, which may negatively influence image classification results. To address these challenges, this paper proposes a novel end-to-end Excitation-Expansion network, coined as E$^2$Net, to localize entire objects with only image-level labels, which served as the base of most multimedia tasks. The proposed E$^2$Net consists of two key components: Maxout-Attention Excitation (MAE) and Orientation-Sensitive Expansion (OSE). Firstly, MAE module aims to activate non-discriminative localization features while simultaneously recovering discriminative classification cues. To this end, we couple erasing strategy with maxout learning efficiently to facilitate entire-object localization without hurting classification accuracy. Secondly, to address rotation variations, the proposed OSE module expands less salient object parts along with all possible orientations. Particularly, OSE module dynamically combines selective attention banks from various orientated expansions of receptive-field, which introduces additional multi-parallel localization heads. Extensive experiments on ILSVRC 2012 and CUB-200-2011 demonstrate that the proposed E$^2$Net outperforms the previous state-of-the-art WSOL methods and also significantly improves classification performance.

Referring expression comprehension (REC) and segmentation (RES) are two highly-related tasks, which both aim at identifying the referent according to a natural language expression. In this paper, we propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning of REC and RES for the first time. In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks. ASNLS supresses the response of unrelated regions in RES based on the prediction of REC. To validate our model, we conduct extensive experiments on three benchmark datasets of REC and RES, i.e., RefCOCO, RefCOCO+ and RefCOCOg. The experimental results report the significant performance gains of MCN over all existing methods, i.e., up to +7.13% for REC and +11.50% for RES over SOTA, which well confirm the validity of our model for joint REC and RES learning.

Modeling human visual attention mechanism is a fundamental problem for the understanding of human vision, which has also been demonstrated as an important module for various multimedia applications such as image captioning and visual question answering. In this paper, we propose a new probabilistic framework for attention, and introduce the concept ofmode to model the flexibility and adaptability of attention modulation in complex environments. We characterize the correlations between the visual input, the activated mode, the saliency and the spatial allocation of attention via a graphical model representation, based on which we explore the lingual guidance from captioning data for the implementation of a mode-sensitive attention (MSA) model. The proposed framework explicitly justifies the usage of center bias for fixation prediction and can convert an arbitrary learning-based backbone attention model to a more robust multi-mode version. Experimental results on the York120, MIT1003 and PASCAL datasets demonstrate the effectiveness of the proposed method.

Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a layer-wise manner for iteratively pruning and retraining which may not be optimal and may be computation intensive. Besides, these methods are designed for pruning a specific structure, such as filter or block structures without jointly pruning heterogeneous structures. In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner. To accomplish this, we first introduce a soft mask to scale the output of these structures by defining a new objective function with sparsity regularization to align the output of baseline and network with this mask. We then effectively solve the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end-to-end manner. By forcing more scale factors in the soft mask to zero, the fast iterative shrinkage-thresholding algorithm (FISTA) can be leveraged to fast and reliably remove the corresponding structures. Extensive experiments demonstrate the effectiveness of GAL on different datasets, including MNIST, CIFAR-10 and ImageNet ILSVRC 2012. For example, on ImageNet ILSVRC 2012, the pruned ResNet-50 achieves 10.88% Top-5 error and results in a factor of 3.7x speedup. This significantly outperforms state-of-the-art methods.

Weakly supervised learning has attracted growing research attention due to the significant saving in annotation cost for tasks that require intra-image annotations, such as object detection and semantic segmentation. To this end, existing weakly supervised object detection and semantic segmentation approaches follow an iterative label mining and model training pipeline. However, such a self-enforcement pipeline makes both tasks easy to be trapped in local minimums. In this paper, we join weakly supervised object detection and segmentation tasks with a multi-task learning scheme for the first time, which uses their respective failure patterns to complement each other's learning. Such cross-task enforcement helps both tasks to leap out of their respective local minimums. In particular, we present an efficient and effective framework termed Weakly Supervised Joint Detection and Segmentation (WS-JDS). WS-JDS has two branches for the above two tasks, which share the same backbone network. In the learning stage, it uses the same cyclic training paradigm but with a specific loss function such that the two branches benefit each other. Extensive experiments have been conducted on the widely-used Pascal VOC and COCO benchmarks, which demonstrate that our model has achieved competitive performance with the state-of-the-art algorithms.

In this paper, we study the problem of zero-shot fine-grained recognition. It aims to distinguish unseen subordinate categories through some other seen categories within an entry-level category. We demonstrate the necessity to learn multiple latent dictionaries through joint training with specific set of instances, human-defined attributes and the class labels. A novel approach that is capable of 1) automatically assigning suitable dictionaries for each instance and 2) learning similarity-specific semantic representations for zero-shot fine-grained recognition is proposed. Experimental results on three benchmark datasets demonstrate that the proposed method achieves superior or comparable performance.

The cross-modality topic detection in social media retains as an open problem mainly due to the difficulty of dealing with modality independence and modality missing. In this paper, we present a novel Deep Topical Correlation Analysis (DTCA) approach, which achieves robust and accurate topic detecting for micro-blogs and handles aforementioned challenges simultaneously. In particular, bidirectional recurrent neural networks and convolutional neural networks are used to learn deep textual and visual features, respectively. Then a Canonical Correlation Analysis based fusion scheme is proposed, which has two innovations to deal with both two problems mentioned above. We further release a large-scale cross-modal twitter dataset for topic detection. Extensive and quantitative evaluations are conducted with comparisons to several state-of-the-arts and alternative approaches on this dataset. Significant performance gains are reported to demonstrate the merits of proposed approach.

Vehicle detection and recognition in remote sensing images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized zero-shot vehicle detection, which is challenging due to the requirement of recognizing vehicles that are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then recognizes vehicles in a coarse-grained manner. Additionally, the hierarchical DeepLab v3 model is beneficially compatible to combine the generalized zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation. The experimental results show that the proposed framework yields promising results on the imperative yet difficult task of zero-shot vehicle detection and recognition.

Deep convolutional neural networks (DCNN) with manifold embedding have achieved considerable attention in computer vision. However, prior arts are usually based on the neighborhood-based graph modeling only the pairwise relationship between two samples, which fail to fully capture intra-class variations and thus suffer from severe performance loss for noisy data. While such intra-class variations can be well captured via sophisticated hypergraph structure, we are motivated and lead a hypergraph induced Convolutional Manifold Network (H-CMN) to significantly improve the representation capacity of DCNN for the complex data. Specifically, two innovative designs are provides: 1) our manifold preserving method is implemented based on a mini-batch, which can be efficiently plugged into the existing DCNN training pipelines and be scalable for large datasets; 2) a robust hypergraph is built for each mini-batch, which not only offers a strong robustness against typical noise, but also captures the variances from multiple features. Extensive experiments on the image classification task on large benchmarking datasets demonstrate that our model achieves much better performance than the state-of-the-art

Microblog sentiment prediction has attracted extensive research focus with wide application prospects. With the increasing proportion of multimodal tweets consisting of images, texts, and emoticons, new challenges have been raised to the existing sentiment prediction schemes. More crucially, it remains an open problem to model the dependency among multiple modalities, where one or more modalities may be missing. In this paper, we present a novel Bi-layer Multimodal Hypergraph learning (Bi-MHG) toward robust sentiment prediction of multimodal tweets to tackle the above challenges. In particular, we design a two-layer structure for the proposed Bi-MHG model: The first layer, that is, a tweet-level hypergraph, learns the tweet-feature correlation and the tweet relevance to predict the sentiments of unlabeled tweets. The second layer, that is, a feature-level hypergraph learns the relevance among different feature modalities (including the midlevel visual features in Sentibank [1]) by leveraging prior multimodal sentiment dictionaries. These two layers are connected by sharing the relevance of multimodal features in a unified bilayer learning scheme. In such a way, Bi-MHG explicitly models the modality relevance rather than implicitly weighting multimodal features adopted in the existing Multimodal Hypergraph learning [2]. Finally, a nested alternating optimization is further proposed for Bi-MHG parameter learning. We have carried out extensive evaluations on a real-world microblog dataset crawled from Sina Weibo. For the task of multimodal sentiment prediction, superior performance is reported over several state-of-the-art and alternative approaches, which demonstrates the merits of the proposed scheme.

Single image denoising and super-resolution are sitting in the core of various image processing and pattern recognition applications. Typically, these two tasks are handled separately, without regarding to joint reinforcement and learning. The former deals with equal-size pixel-to-pixel translation, while the latter deals with scaling up amount of input pixels. In this paper, we propose a Generative Adversarial Network(GAN) towards joint learning of single image denoising and super-resolution. In principle, our design allows both tasks to share several common building blocks, with the linking between both outputs to reinforce each other. Such a reinforcement is accomplished via designing a novel generative network through optimizing a novel loss function to achieve both denoising and super-resolution. Quantitatively comparing to a set of alternative approaches and baselines, the experiment demonstrated superior performance our method in denoising and super-resolution with high upscaling factors.

Given the difficulty in labeling sufficient amount of instances across different resolutions and imaging environment of satellite images, weakly supervised vehicle detection is with great importance for satellite images analysis and processing. To prevent such cumbersome and meticulous manual annotation, naturally we have introduced the weakly supervised detection that has recently explosively prevalent in ordinary viewing angle images. Our program merely stands in need of region-level group annotation, i.e., whether this district convers vehicle(s) without plainly pointing out the coordinates of vehicles. There are two major problems are often encountered for Weakly Supervised Object Detection. One is that it is often chooses only a most expressive instance contains multiple target objects which often have a bigger probability when selecting a target block. For this problem, the number of vehicles can be estimated based on the object counting, a combinatorial selection algorithm can be used to select patch which contains at most one vehicle instance. Another problem is that precise object positioning becomes more difficult due to the lack of instance-level supervision. This problem can be optimized by a progressive learning strategy. Experiments was carried on wide-ranging remote sensing dataset and achieved better results compared to the state-of-the-art weakly supervised vehicle detection schemes.

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Vehicle detection in satellite image has attracted extensive research attentions with various emerging applications.However, the detector performance has been significantly degenerated due to the low resolutions of satellite images, as well as the limited training data.In this paper, a robust domain-adaptive vehicle detection framework is proposed to bypass both problems.Our innovation is to transfer the detector learning to the high-resolution aerial image domain,where rich supervision exists and robust detectors can be trained.To this end, we first propose a super-resolution algorithm using coupled dictionary learning to ``augment'' the satellite image region being tested into the aerial domain.Notably, linear detection loss is embedded into the dictionary learning, which enforces the augmented region to be sensitive to the subsequent detector training.Second, to cope with the domain changes, we propose an instance-wised detection using Exemplar Support Vector Machines (E-SVMs), which well handles the intra-class and imaging variations like scales, rotations, and occlusions.With comprehensive experiments on large-scale satellite image collections, we demonstrate that the proposed framework can significantly boost the detection accuracy over several state-of-the-arts.

Coastline extraction in Synthetic aperture radar (SAR) images is a fundamental and challenging task due to the speckle noise. In this paper, we propose a new method for automatic coastline extraction in SAR images. In our method, we combine K-means and speckle noise removal methods together to increase the dissimilarity between sea and land. To enhance the robustness to speckle noise, and preserve the targets boundaries, we treat superpixels as basic regions instead of pixels in traditional pixel-based methods. Finally, an adaptive threshold is applied to classify these regions into sea or land. Based on the classifications, a canny detector is employed to detect the coastline. We evaluate our proposed method on SAR images and the improved coastline extraction method superpixel-based is verified on remote sensing images with RGB channels. The experimental results demonstrate its superior performance on coastline extraction.

This paper presents a study of vehicle detection from high-resolution aerial images. In this paper, a superpixel segmentation method designed for aerial images is proposed to control the segmentation with a low breakage rate. To make the training and detection more efficient, we extract meaningful patches based on the centers of the segmented superpixels. After the segmentation, through a training sample selection iteration strategy that is based on the sparse representation, we obtain a complete and small training subset from the original entire training set. With the selected training subset, we obtain a dictionary with high discrimination ability for vehicle detection. During training and detection, the grids of histogram of oriented gradient descriptor are used for feature extraction. To further improve the training and detection efficiency, a method is proposed for the defined main direction estimation of each patch. By rotating each patch to its main direction, we give the patches consistent directions. Comprehensive analyses and comparisons on two data sets illustrate the satisfactory performance of the proposed algorithm.

In this paper, we present a novel aperiodic directional structure measurement (ADSM) toward road network extraction. Based on the observations from Cognitive Psychology regarding the aperiodicity and local directionality, ADSM can well characterize roadlike structures independent of the spectral character and contrast. By exploiting such measurement as guidance, we construct a mask to denote potential road regions. Then, by combining with some common morphology operators, our approach is able to provide robust road centerlines efficiently. We evaluate our approach with data from various satellite sensors and make comprehensive comparisons with previous state-of-the-art methods. Experimental results demonstrate the merit using our ADSM as a metric to identify potential road structures, as well as the effectiveness and efficiency of our road network extraction system.

This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and a multiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.

Interactive mobile vision applications, such as Mobile Landmark Recognition (MLR), have recently attracted ever increasing research attention due to the exponential growth of mobile devices. However, the recognition accuracy retains as a bottleneck hesitating the proliferation of such applications. To address this challenge, in this paper we design a novel framework based on interactive image segmentation and multiple visual features fusion to improve the accuracy of on-device MLR systems. Firstly, we propose a simple but effective vector binarization method to reduce the memory usage of image description significantly without decreasing the search accuracy. Secondly, we design a location aware fusion algorithm which can integrate multiple visual features into a compact yet discriminative image descriptor on-device. Thirdly, a user-friendly interaction scheme is developed to enable interactive foreground/background segmentation to improve the recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm for on-device MLR applications.

In this paper, a rapid oil spill detection approach which still maintains high detection accuracy is presented. The major contribution of the approach is using a superpixel segmentation method to subdivide the target SAR image into many approximate uniform scale pieces and preserves the boundaries well. Furthermore, a novel approach combine space distance, intensity deviation and size information together (SIS) is presented to eliminate the potential false positive, which is convenient and effective meanwhile. The proposed approach performs well and fast in both the synthetic data and RAD ARS AT-1 ScanSAR data which contain verified oil spills. The processing time is about 6s for a 512×512 image.

Most recently the Bag-of-Features (BoF) representation has been well advocated for image search and classification, with two decent phases named sparse coding and max pooling to compensate quantization loss as well as inject spatial layouts. But still, much information has been discarded by quantizing local descriptors with two-dimensional layouts into a one-dimensional BoF histogram. In this paper, we revisit this popular “sparse coding + max pooling” paradigm by “looking around” the local descriptor context towards an optimal BoF. First, we introduce a Weakly supervised Sparse Coding (WSC) to exploit the Classemes-based attribute labeling to refine the descriptor coding procedure. It is achieved by learning an attribute-to-word co-occurrence prior to impose a label inconsistency distortion over the ℓ1based coding regularizer, such that the descriptor codes can maximally preserve the image semantic similarity. Second, we propose an adaptive feature pooling scheme over “superpixels” rather than over fixed spatial pyramids, named Geometric Consistency Pooling (GCP). As an effect, local descriptors enjoying good geometric consistency are pooled together to ensure a more precise spatial layouts embedding in BoF. Both of our phases are unsupervised, which differ from the existing works in supervised dictionary learning, sparse coding and feature pooling. Therefore, our approach enables potential applications like scalable visual search. We evaluate in both image classification and search benchmarks and report good improvements over the state-of-the-arts.

Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to duplicate the user browsing. Therefore, their diversification and grouping are highly demanded to improve the user experiences. However, the tagging and content qualities of user contributed videos are uncontrolled against their precise grouping. In this paper, we present a weakly supervised topic grouping paradigm to diversify the returned videos of a given keyword query. Our grouping is based on the bag-of-words visual signature quantized over the spatiotemporal STIP descriptor [1] extracted from each returned video. First, we adopt a min-Hashing based visual similarity in combination of the tagging similarity to group the returned videos. Based on the initial grouping configurations, we mine the co-occurred discriminative sub-signatures, based on which we iteratively refine the first step. Such iteration well handles the noise in visual content and tagging, since neither of which is fully trusted during the grouping. We validate our schemes on over 2,000 video clips crawled from a set of YouTube keyword query results. Comparing to alternative approaches, our scheme has shown superior robustness and precision.

Reversible watermarking aims to restore the original data after watermark extraction, which is more suitable for copyright protection of 2D-vector maps. In this paper, we present a reversible watermarking strategy for 2D-vector maps based on iterative embedding. It begins with vertex grouping of each polyline. Then only the highly correlated data sets are selected as the cover data for iterative embedding. Finally, the iterative embedding is carried out by reversibly modifying the median vertex coordinates of each selected embedding unit. The original vector data can be strictly recovered with accurate watermark extraction. Meanwhile, both higher payload capacity and better invisibility are proved through both theoretical analysis and comprehensive experimental validations. Experimental results show that the proposed reversible watermarking method is very suitable for 2D-vector map copyright protection and secret communication.

This paper presents a perception-driven, block-based wavelet watermarking algorithm, which merits in both perceptual invisibility and robustness against watermark attacking. First, attention-based region saliency analysis is adopted to select perceptually unconcerned regions for watermark embedding. Second, wavelet transform is carried out to embed secret message into high frequency coefficients within the perceptually unconcerned regions. Especially, we present an optimal block mapping substitution method, which adopts genetic algorithm to reduce PSNR cost. Its main idea is to minimize the degradation of the watermarked image by learning a best mapping function between original image blocks and secret messages at global scope. Extensive experiments are conducted to validate both the perceptual invisibility and robustness against Gaussian Noise and JPEG compression attacks. We also give PSNR comparison with state-of-the-art methods to demonstrate our advantages.

This paper presents an effective and reversible watermarking approach for digital copyright protection of 2D-vector maps. To ensure that the embedded watermark is insensitive for human perception, we only select the noise non-sensitive regions for watermark embedding by estimating vertex density within each polyline. To ensure the exact recovery of original 2D-vector map after watermark extraction, we introduce a new reversible watermarking scheme based on reversible high-frequency wavelet coefficients modification. Within the former-selected non-sensitive regions, our watermarking operates on the lower-order vertex coordinate decimals with integer wavelet transform. Such operation further reduces the visual distortion caused by watermark embedding. We have validated the effectiveness of our scheme on our real-world city river/building 2D-vector maps. We give extensive experimental comparisons with state-of-the-art methods, including embedding capability, invisibility, and robustness over watermark attacking.