Long-term action anticipation from egocentric video is critical for applications such as human-computer interaction and assistive technologies, where anticipating user intent enables proactive and context-aware AI assistance. However, existing approaches suffer from three key limitations: 1) underutilization of fine-grained visual cues from hand-object interactions, 2) neglect of semantic dependencies between verbs and nouns, and 3) lack of explicit cognitive reasoning, limiting generalization and long-term forecasting ability. To overcome these challenges, we propose INSIGHT, a unified two-stage framework for egocentric action anticipation. In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions and enhances action representations using a verb-noun co-occurrence matrix. In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning through a structured process: visual perception (think) → intention inference (reason) → action anticipation (answer). Extensive experiments on Ego4D, EPIC-Kitchens-55, and EGTEA Gaze+ benchmarks show that INSIGHT achieves state-of-the-art performance, demonstrating its effectiveness and strong generalization capability.

With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing attention from researchers. Unlike unimodal video retrieval methods, the CVR task takes a multi-modal query consisting of a reference video and a piece of modification text as input. The modification text conveys the user's intended alterations to the reference video. Based on this input, the model aims to retrieve the most relevant target video. In the CVR task, there exists a substantial discrepancy in information density between video and text modalities. Traditional composition methods tend to bias the composed feature toward the reference video, which leads to suboptimal retrieval performance. This limitation is significant due to the presence of three core challenges: (1) modal contribution entanglement, (2) explicit optimization of composed features, and (3) retrieval uncertainty. To address these challenges, we propose the evidence-dRivEn dual-sTream diRectionAl anChor calibration networK (ReTrack). ReTrack is the first CVR framework that improves multi-modal query understanding by calibrating directional bias in composed features. It consists of three key modules: Semantic Contribution Disentanglement, Composition Geometry Calibration, and Reliable Evidence-driven Alignment. Specifically, ReTrack estimates the semantic contribution of each modality to calibrate the directional bias of the composed feature. It then uses the calibrated directional anchors to compute bidirectional evidence that drives reliable composed-to-target similarity estimation. Moreover, ReTrack exhibits strong generalization to the Composed Image Retrieval (CIR) task, achieving SOTA performance across three benchmark datasets in both CVR and CIR scenarios.

Video large language models have achieved remarkable performance in tasks such as video question answering, however, their temporal understanding remains suboptimal. To address this limitation, we curate a dedicated instruction fine-tuning dataset that focuses on enhancing temporal comprehension across five key dimensions. In order to reduce reliance on costly temporal annotations, we introduce a multi-task prompt fine-tuning approach that seamlessly integrates temporal-sensitive tasks into existing instruction datasets without requiring additional annotations. Furthermore, we develop a novel benchmark for temporal-sensitive video understanding that not only fills the gaps in dimension coverage left by existing benchmarks but also rigorously filters out potential shortcuts, ensuring a more accurate evaluation. Extensive experimental results demonstrate that our approach significantly enhances the temporal understanding of video-LLMs while avoiding reliance on shortcuts.

AI personal assistants, deployed through robots or wearables, require embodied understanding to collaborate effectively with humans. However, current Multimodal Large Language Models (MLLMs) primarily focus on third-person (exocentric) vision, overlooking the unique challenges of first-person (egocentric) videos. Additionally, high acquisition costs limit data size, impairing MLLM performance. To address these challenges, we propose learning the mapping between exocentric and egocentric domains, leveraging the extensive exocentric knowledge within existing MLLMs to enhance egocentric video understanding. To this end, we introduce Ego-ExoClip, a pre-training dataset comprising 1.1M synchronized ego-exo clip-text pairs derived from Ego-Exo4D, together with the instruction-tuning dataset EgoIT, which is collected from multiple sources to enhance the model's instruction-following capabilities. Building upon the datasets, we propose a migration strategy and further design a progressive mapping learning pipeline with three stages: Demonstrator Self-Preparation, Demonstrator-Learner Guidance, and Learner Self-Practice. Extensive experiments across diverse egocentric tasks reveal that existing MLLMs perform inadequately in egocentric video understanding, while our model significantly outperforms these leading models.

Low-Rank Adaptation (LoRA) has emerged as a powerful parameter-efficient fine-tuning method for adapting large language models to downstream tasks. Recent studies have leveraged Mixture-of-Experts (MoE) mechanism to effectively integrate multiple LoRA modules, facilitating efficient parameter adaptation for multi-task scenarios. It has been shown that fostering knowledge sharing across LoRA experts can greatly enhance parameter adaptation efficiency. However, the existing approach for LoRA expert knowledge sharing still faces two key limitations: constrained functional specialization and induced expert homogenization. To address these issues, we propose a novel diversity-regulated asymmetric MoE-LoRA decomposition framework, which achieves flexible knowledge sharing through asymmetric expert decomposition and guarantees expert diversity with a dual orthogonality regularization. Extensive experiments on eight public benchmarks, spanning both multi-task and single-task settings, demonstrate the superiority of our approach over existing methods.

Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple tokenbased chains-of-thought in parallel and aggregating outcomes through voting or search.Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative to explicit Chain-of-Thought, yet whether such latent models can similarly benefit from parallel TTS remains open, mainly due to the absence of sampling mechanisms in continuous space, and the lack of probabilistic signals for advanced trajectory aggregation.This work enables parallel TTS for latent reasoning models by addressing the above issues.For sampling, we introduce two uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive Gaussian Noise.For aggregation, we design a Latent Reward Model (LatentRM) trained with step-wise contrastive objective to score and guide latent reasoning.Extensive experiments and visualization analyses show that both sampling strategies scale effectively with compute and exhibit distinct exploration dynamics, while LatentRM enables effective trajectory selection.Together, our explorations open a new direction for scalable inference in continuous spaces.

Egocentric Action Recognition (EAR) aims to identify fine-grained actions and interacted objects from first-person videos, forming a core task in egocentric video understanding. Despite recent progress, EAR remains challenged by limited data scale, annotation quality, and long-tailed class distributions. To address these issues, we propose REAR, a Retrieval-augmented framework for EAR that leverages external third-person (exocentric) videos as auxiliary knowledge—without requiring synchronized ego-exo pairs. REAR adopts a dual-branch architecture: one branch extracts egocentric representations, while the other retrieves semantically relevant exocentric features. These are fused via a cross-view integration module that performs staged refinement and attention-based alignment. To mitigate class imbalance, a class-adaptive selector dynamically adjusts retrieval depth based on class frequency, and independent classifiers are trained with logit-adjusted cross-entropy. Extensive experiments across three benchmarks demonstrate that REAR achieves state-of-the-art performance, with significant gains in object recognition and tail-class accuracy. The source code is publicly available at https://github.com/zou-y23/REAR.

The recent advancement in video temporal grounding (VTG) has significantly enhanced fine-grained video understanding, primarily driven by multimodal large language models (MLLMs). With superior multimodal comprehension and reasoning abilities, VTG approaches based on MLLMs (VTG-MLLMs) are gradually surpassing traditional fine-tuned methods. They not only achieve competitive performance but also excel in generalization across zero-shot, multi-task, and multi-domain settings. Despite extensive surveys on general video-language understanding, comprehensive reviews specifically addressing VTG-MLLMs remain scarce. To fill this gap, this survey systematically examines current research on VTG-MLLMs through a three-dimensional taxonomy: 1) the functional roles of MLLMs, highlighting their architectural significance; 2) training paradigms, analyzing strategies for temporal reasoning and task adaptation; and 3) video feature processing techniques, which determine spatiotemporal representation effectiveness. We further discuss benchmark datasets, evaluation protocols, and summarize empirical findings. Finally, we identify existing limitations and propose promising research directions.

With the popularity of image editing techniques, synthetic images may have inharmonious regions due to color/illumination differences between the manipulated area and the background. The inharmonious region localization task aims to find these regions, which is crucial for blind image harmonization. Existing methods rely on single-view images and do not fully explore multi-scale fusion, which limits their performance. To address these issues, in this paper, we propose a novel multi-view perception and shrinkage aggregation network (MSANet) for the inharmonious region localization task that fully utilizes multi-view images and multi-scale fusion information and can mine subtle cues between candidate objects and the background. Specifically, we first design a multi-view ensemble encoder to fully perceive the inharmonious regions by multi-view interactive learning and then aggregate the feature representations of inharmonious regions. Moreover, we propose a multi-scale shrinkage fusion decoder, where multi-scale features with multi-view prior information are utilized to aggregate adjacent features, adaptively select high-quality information, reduce background interference and gradually locate inharmonious regions. Extensive experimental results on four public datasets (HDobe5K, HCOCO, HFlickr, and Hday2Night) demonstrate that the proposed MSANet can outperform all the SOTA methods in terms of average F1 and average IoU score, while maintaining a lower computational cost1.

In egocentric videos, collecting and annotating supervised data is more complicated and time-consuming than in exocentric videos, limiting research in this area. As a remedy, Unsupervised Domain Adaptation (UDA) enhances model performance on unlabeled target domains by bridging the distribution gap between source and target domains. However, UDA for egocentric action recognition is under-explored, facing unique challenges such as simultaneous learning of verb and noun representations, focusing on human-object interactions, and managing excessive verb-noun combinations. To tackle these issues, we propose a novel Unsupervised Domain Adaptation for Egocentric Action Recognition (UDA-EAR) approach that adaptively models egocentric actions and facilitates cross-domain knowledge transfer, improving recognition performance in unlabeled target domains. Specifically, our UDA-EAR employs adaptive spatio-temporal and spatio-channel attention in a dual-branch pipeline to focus on motion intervals and interaction regions, respectively, allowing specialized learning of discriminative representations while avoiding negative combination dependencies from domain gaps. Additionally, an adversarial domain alignment mechanism aligns the data distributions between source and target domains, effectively transferring fine-grained verb-noun knowledge of egocentric videos. Extensive experiments demonstrate that our UDA-EAR outperforms state-of-the-art baselines on widely used egocentric datasets, significantly improving egocentric action recognition accuracy. Our source codes and datasets are available at https://github.com/zou-y23/UDA-EAR.

Unlike conventional visual question answering, video-grounded dialog requires a deep understanding of both the dialog history and the video content to generate accurate responses. Although existing methods have achieved promising results, they still struggle with progressively comprehending complex dialog history and effectively integrating video information. To address these challenges, we propose an iterative search and reasoning framework composed of a textual encoder, a visual encoder, and a generator. Specifically, the textual encoder adopts a path search and aggregation strategy to identify key cues in the dialog history that are essential for understanding the current question. Meanwhile, the visual encoder employs an iterative reasoning network to extract and highlight critical visual evidence from the video, thereby enabling more comprehensive visual understanding. Finally, we use a pretrained GPT-2 model as the answer generator to transform the discovered latent cues into coherent and contextually appropriate responses. Extensive experiments on three public datasets demonstrate the effectiveness and generalizability of the proposed framework.

Egocentric video grounding is a crucial task for embodied intelligence applications, distinct from exocentric video moment localization. Existing methods primarily focus on the distributional differences between egocentric and exocentric videos but often neglect key characteristics of egocentric videos and the fine-grained information emphasized by question-type queries. To address these limitations, we propose OSGNet, an Object-Shot enhanced Grounding Network for egocentric video. Specifically, we extract object information from videos to enrich video representation, particularly for objects highlighted in the textual query but not directly captured in the video features. Additionally, we analyze the frequent shot movements inherent to egocentric videos, leveraging these features to extract the wearer’s attention information, which enhances the model’s ability to perform modality alignment. Experiments conducted on three datasets demonstrate that OSGNet achieves state-of-the-art performance, validating the effectiveness of our approach. Our code can be found at https://github.com/Yisen-Feng/OSGNet.

In this paper, we address the novel task of egocentric modality generalization action recognition, which aims to learn a unified discrete representation from paired multimodal egocentric action data during pre-training. This approach enables cross-modal zero-shot generalization in downstream tasks, where the modalities available during inference and training are disjoint. While recent efforts have focused on aligning instance-level or temporal features to reduce feature distribution discrepancies across modalities, they have overlooked the inherent structural categorization within action data. To address this limitation, we propose Modal-Agnostic Prototype Learning (MAPLE), a framework that leverages a prototype memory bank to capture categorical structures. This is further enhanced by a robust semantic disentanglement module and a moment aggregation mechanism, enabling semantically similar behaviors to cluster more closely in the latent space and promoting robust cross-modal generalization. Extensive experiments on the Ego4D and WEAR datasets demonstrate that MAPLE significantly outperforms existing approaches, marking a substantial advancement in the field of egocentric action recognition.

Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification text. As previous supervised or zero-shot learning paradigms all fail to strike a good trade-off between the model’s generalization ability and retrieval performance, recent researchers have introduced the task of few-shot CIR (FS-CIR) and proposed a textual inversion-based network based on pretrained CLIP model to realize it. Despite its promising performance, the approach encounters two key limitations: simply relying on the few annotated samples for CIR model training and indiscriminately selecting training triplets for CIR model fine-tuning. To address these two limitations, we propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we propose an attentive masking and captioning-based pseudo triplet generation method, to construct pseudo triplets from pure image data and use them to fulfill the CIR-task specific pretraining. In the second stage, we propose a challenging triplet-based CIR fine-tuning method, where we design a pseudo modification text-based sample challenging score estimation strategy and a robust top range-based random sampling strategy for sampling robust challenging triplets to promote the model fine-tuning. Notably, our scheme is plug-and-play and compatible with any existing supervised CIR models. We test our scheme across two backbones on three public datasets (i.e., FashionIQ, CIRR, and Birds-to-Words), achieving maximum improvements of 13.3%, 22.2%, and 17.4% respectively, demonstrating our scheme’s efficacy.

Visual-spatial understanding, the ability to infer object relationships and layouts from visual input, is fundamental to downstream tasks such as robotic navigation and embodied interaction. However, existing methods face spatial uncertainty and data scarcity, limiting the 3D spatial reasoning capability of pre-trained vision-language models (VLMs). To address these challenges, we present a unified framework for enhancing 3D spatial reasoning in pre-trained VLMs without modifying their architecture. This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes through an automated construction process designed for fine-tuning. Extensive experiments across multiple benchmarks demonstrate the individual and combined effectiveness of our prompting and fine-tuning strategies, and yield insights that may inspire future research on visual-spatial understanding.

Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal image retrievers, effectively addressing diverse real-world image retrieval tasks.Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs' retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR).Therefore, in this work, we propose an automated fine-grained multimodal quintuple dataset construction pipeline and a novel two-stage fine-grained multimodal fine-tuning strategy.The dataset generation pipeline produces a comprehensive CIR dataset with fine-grained image captions and modification text, facilitating fine-grained context modeling.Beyond the previously entangled fine-tuning paradigm, our approach separates the fine-tuning process into two distinct stages: (1) fine-grained context reasoning-oriented fine-tuning and (2) fine-grained retrieval-oriented fine-tuning.These stages aim to sequentially enhance the model's context understanding and query-target alignment capabilities, thereby improving retrieval performance.Extensive experiments across five datasets encompassing diverse and complex image retrieval tasks demonstrate the remarkable superiority of our method over existing approaches in *Xuemeng Song (sxmustc

Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple modalities (i.e.,utterance, video, and audio). Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting the sentiments residing in the utterance, video and audio, which play important roles in reflecting sarcasm that essentially involves subtle sentiment contrasts. Nevertheless, it is non-trivial to incorporate sentiments for boosting SED performance, due to three main challenges: 1) diverse effects of utterance tokens on sentiments; 2) gap between video-audio sentiment signals and the embedding space of BART; and 3) various relations among utterances, utterance sentiments, and video-audio sentiments. To tackle these challenges, we propose a novel sEntiment-enhanceD Graph-based multimodal sarcasm Explanation framework, named EDGE. In particular, we first propose a lexicon-guided utterance sentiment inference module, where a heuristic utterance sentiment refinement strategy is devised. We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip. Thereafter, we devise a context-sentiment graph to comprehensively model the semantic relations among the utterances, utterance sentiments, and video-audio sentiments, to facilitate sarcasm explanation generation. Extensive experiments on the publicly released dataset WITS verify the superiority of our model over cutting-edge methods.

In recent years, the use of large-scale pre-trained models for vision-language tasks has gained significant attention and has shown promising results in the video question answering. However, the increasing size of these models has made the fully fine-tuning strategy impractical. Therefore, there is a growing need for research in parameter-efficient transfer learning for downstream tasks. To address this challenge, we introduce a novel parameter-efficient transfer learning technique based on a temporal reasoning adapter for the video question answering task. Our proposed approach captures the temporal relationship within videos, enabling the model to possess visual reasoning ability and knowledge acquisition ability from language models. Our extensive experiments on four video question answering datasets indicate that our method can match or even outperform fully fine-tuning strategies and state-of-the-art models, while having the advantage of parameter efficiency.

The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. MFAS integrates a patch partition and merging module for enhanced small object recognition, a prior-guided patch selection module for noise suppression and focused analysis, and a hierarchical aggregation network to aggregate visual semantics guided by questions. Extensive experiments on several public egocentric datasets have validated the effectiveness and generalization of our framework. Code and data are available in https://github.com/Hyu-Zhang/EgoVideoQA.

Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-shot CIR (ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer ZS-CIR studies focus on converting the CIR task into a standard text-to-image retrieval task by pre-training a textual inversion network that can map a given image into a single pseudo-word token. Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained pseudo-word token mapping and tri-wise caption-based semantic regularization. The former maps the image into a subject-oriented pseudo-word token and several attribute-oriented pseudo-word tokens to comprehensively express the image in the textual form, while the latter works on jointly aligning the fine-grained pseudo-word tokens to the real-word token embedding space based on a BLIP-generated image caption template. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed method.

The objective of the occluded person re-identification (ReID) task is to capture the same person from different camera angles when the pedestrian’s body is partially occluded. In this task, there are two main challenges: 1) pedestrians are often occluded by other persons or objects, and 2) pedestrians change poses. Moreover, these two issues often simultaneously occur. Although many occluded person ReID algorithms have been proposed, many existing methods can often only solve one of these issues well, and the other issue is often ignored. In this work, a novel semantic perception and CNN-transformer hybrid network (abbreviated as SPH) is proposed for occluded person ReID, which consists of a CNN-based human semantic perception stream and a transformer-based pose perception stream. In the former, a human semantic auxiliary module and a human semantic perception module are designed to obtain human semantic information where multi-granularity region features of the human body are extracted to solve the issues of occlusion. In the latter, we propose a token-based pose integration module to obtain the corresponding patch for each pose key-point and the relative position information to solve the change in pedestrian pose. Moreover, these two streams are jointly optimized in a unified framework. In addition, to further solve the issue of occlusion, the human completion strategy is proposed for the query sample where the gallery samples are used to complete the missing parts of the query. Extensive experimental results on three public occluded person ReID datasets, Occluded-DukeMTMC, P-DukeMTMC-reID, and Occluded-REID, demonstrate that the proposed method can outperform all SOTA occluded person ReID methods in terms of the mAP and Rank-1. Compared with PAT (CVPR21) on the Occluded-DukeMTMC and Occluded-REID datasets, the improvements in mAP/Rank-1 reached 10.1%/7.4%, and 10%/1%, respectively. Moreover, when TransReID (ICCV21) was used, SPH achieved improvements of 4.5% (mAP) and 5.5% (Rank-1) on the Occluded-DukeMTMC dataset.

Image sequence interpolation is a critical research area in computer vision with broad applications in video frame interpolation and medical image interlayer interpolation. Traditional deep learning-based methods in this domain predominantly rely on deep convolutional neural networks (CNNs), which, despite their effectiveness, are limited by the inherent constraints of CNN architecture, impacting their interpolation accuracy. To address these limitations, we introduce the Pre-ISIformer, a parallel multi-channel adaptive image sequence interpolation network founded on pre-trained transformers. This innovative network is composed of three integral modules: 1) Global feature extraction module is designed to extract primary features from the input images using a pre-trained Swin-transformer model, ensuring comprehensive global feature coverage. 2) Feature sequence construction module adaptively decomposes the object’s motion path across different frames, facilitating a detailed analysis of motion dynamics. And 3) Intermediate image reconstruction module is responsible for accurately capturing target displacements. Furthermore, we incorporate distinct metrics for pixel loss and gradient loss to meticulously reconstruct the texture and contours of the intermediate images. Our network has been rigorously tested on various datasets for two primary applications: video frame interpolation and interlayer interpolation in medical imaging. The results from these experiments showcase the superior performance and effectiveness of the Pre-ISIformer, establishing it as a significant advancement in the field of image sequence interpolation.

Talking head generation, aiming to create photo-realistic videos from a single reference image and audio input, has emerged as a vibrant area of interest within the computer vision community. Despite notable advancements, several challenges remain unaddressed. For instance, many existing approaches overlook the nuanced relationship between audio semantics and head movement, such as nodding in agreement during affirmative expressions. Additionally, the visual quality of generated content, particularly in depicting teeth, often falls short of achieving authentic realism. To address these limitations, we introduce a groundbreaking audio-semantic enhanced pose-driven talking head generation method. Our approach encompasses a multimodal 3DMM parameter prediction network alongside a high-fidelity video synthesis network, meticulously designed to produce authentic and high-quality talking head videos. The multimodal 3DMM parameter prediction network harnesses both acoustic and audio-deduced semantic information, facilitating accurate head pose predictions that resonate with the semantics of spoken words. Furthermore, to significantly improve the depiction of the mouth area, especially the teeth, our video synthesis stage incorporates a mouth-enhanced network augmented by both local and global discriminators. Comprehensive evaluations across diverse metrics affirm the superiority of our method.

Video semantic role grounding has gained substantial interest from both the academic and industrial communities. While existing methods have demonstrated considerable performance improvements, the influence of noisy and intra-object proposals, referring to proposals with the same object label, has yet to be explored in video semantic role grounding. In this study, we propose a semantic-aware contrastive learning network with proposal suppression to enhance the accuracy of grounding referenced objects. To fully exploit the semantic information in each semantic role, we introduce a novel semantic role encoding module that allows for precise representations of each semantic role. We also design a semantic-aware proposal suppression network to reduce the impact of noisy proposals on object representation learning. Additionally, we propose a proposal contrastive loss to improve cross-modal alignment and reduce the effect of irrelevant intra-object proposals. Extensive experiments on four datasets demonstrate that our model achieves significant improvements over state-of-the-art methods.

Free-viewpoint human body reenactment aims to generate authentic and coherent poses for a source subject based on a target body pose skeleton. While current methods are proficient in reproducing existing poses, they falter in generating novel poses, often yielding blurry results. In this paper, we propose a method for open-set pose synthesis, utilizing multi-view images to generate novel poses from arbitrary viewpoints. Our method begins with building the neural radiance fields (NeRF) using multi-view images. While this NeRF is adept at rendering specific free-viewpoint refined poses, it struggles with sharp results for novel poses. To address this, we introduce a 2D novel pose diffusion (2D-NPD) module and a view-consistent NeRF optimization (VCNeRF-O) strategy. The 2D-NPD performs body reenactment in the 2D domain to generate a set of refined novel pose images. In particular, we introduce a motion adapter tailored for the stable diffusion (SD) model to generate novel poses while preserving the cloth texture. To ensure seamless motion and image clarity, we further devise a dual warp loss function for the motion adapter. Moreover, to generate fine-grained novel poses while maintaining viewpoint consistency, we develop an innovative VCNeRF-O to optimize the NeRF. Experiments demonstrate that our approach outperforms existing techniques in terms of texture quality and consistency in the open-set synthesis of novel poses.

The goal of talking face generation is to synthesize a sequence of face images of the specified identity, ensuring the mouth movements are synchronized with the given audio. Recently, image-based talking face generation has emerged as a popular approach. It could generate talking face images synchronized with the audio merely depending on a facial image of arbitrary identity and an audio clip. Despite the accessible input, it forgoes the exploitation of the audio emotion, inducing the generated faces to suffer from emotion unsynchronization, mouth inaccuracy, and image quality deficiency. In this article, we build a bistage audio emotion-aware talking face generation (AMIGO) framework, to generate high-quality talking face videos with cross-modally synced emotion. Specifically, we propose a sequence-to-sequence (seq2seq) cross-modal emotional landmark generation network to generate vivid landmarks, whose lip and emotion are both synchronized with input audio. Meantime, we utilize a coordinated visual emotion representation to improve the extraction of the audio one. In stage two, a feature-adaptive visual translation network is designed to translate the synthesized landmarks into facial images. Concretely, we proposed a feature-adaptive transformation module to fuse the high-level representations of landmarks and images, resulting in significant improvement in image quality. We perform extensive experiments on the multi-view emotional audio-visual dataset (MEAD) and crowd-sourced emotional multimodal actors dataset (CREMA-D) benchmark datasets, demonstrating that our model outperforms state-of-the-art benchmarks.

Localizing a desired moment within an untrimmed video via a given natural language query, i.e., cross-modal moment localization, has attracted widespread research attention recently. However, it is a challenging task because it requires not only accurately understanding intra-modal semantic information, but also explicitly capturing inter-modal semantic correlations (consistency and complementarity). Existing efforts mainly focus on intra-modal semantic understanding and inter-modal semantic alignment, while ignoring necessary semantic supplement. Consequently, we present a cross-modal semantic perception network for more effective intra-modal semantic understanding and inter-modal semantic collaboration. Concretely, we design a dual-path representation network for intra-modal semantic modeling. Meanwhile, we develop a semantic collaborative network to achieve multi-granularity semantic alignment and hierarchical semantic supplement. Thereby, effective moment localization can be achieved based on sufficient semantic collaborative learning. Extensive comparison experiments demonstrate the promising performance of our model compared with existing state-of-the-art competitors.

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-aligned. The most promising way is to collect image-text pairs from the Internet, but it will inevitably introduce noisy correspondence. To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models. Specifically, we use the Gaussian Mixture model to model the similarity obtained by CLIP as clean distribution and noisy distribution, to filter out most of the noisy correspondence in the dataset. Afterward, we used relatively clean data to fine-tune the model. To further reduce the negative impact of unfiltered noisy correspondence, i.e., a minimal part where two distributions intersect during the fine-tuning process, we propose a distribution-sensitive dynamic margin ranking loss, further increasing the distance between the two distributions. Through continuous iteration, the noisy correspondence gradually decreases and the model performance gradually improves. Our extensive experiments demonstrate the effectiveness and robustness of our model even under high noise rates.

Video question answering, aiming to answer a natural language question related to the given video, has become prevalent in the past few years. Although remarkable improvements have been obtained, it is still exposed to the challenge of insufficient comprehension of video content. To this end, we propose a spatial-temporal representative visual exploitation network for video question answering, which enhances the understanding of the video by merely summarizing representative visual information. In order to explore representative object information, we advance adaptive attention based on uncertainty estimation. At the same time, to capture representative frame-level and clip-level visual information, we structure a much more compact set of representations iteratively in an expectation-maximization manner to deprecate noisy information. Both the quantitative and qualitative results on NExT-QA, TGIF-QA, MSRVTT-QA, and MSVD-QA datasets demonstrate the superiority of our model over several state-of-the-art approaches.

Accurate and automated segmentation of lesions in brain MRI scans is crucial in diagnostics and treatment planning. Despite the significant achievements of existing approaches, they often require substantial computational resources and fail to fully exploit the synergy between low-level and high-level features. To address these challenges, we introduce the Separable Spatial Convolutional Network (SSCN), an innovative model that refines the U-Net architecture to achieve efficient brain tumor segmentation with minimal computational cost. SSCN integrates the PocketNet paradigm and replaces standard convolutions with depthwise separable convolutions, resulting in a significant reduction in parameters and computational load. Additionally, our feature complementary module enhances the interaction between features across the encoder-decoder structure, facilitating the integration of multi-scale features while maintaining low computational demands. The model also incorporates a separable spatial attention mechanism, enhancing its capability to discern spatial details. Empirical validations on standard datasets demonstrate the effectiveness of our proposed model, especially in segmenting small and medium-sized tumors, with only 0.27M parameters and 3.68 GFlops. Our code is available at https://github.com/zzpr/SSCN .

This article introduces the Universal Relocalizer, a novel approach designed for weakly supervised referring expression grounding. Our method strives to pinpoint a target proposal that corresponds to a specific query, eliminating the need for region-level annotations during training. To bolster the localization precision and enrich the semantic understanding of the target proposal, we devise three key modules: the category module, the color module, and the spatial relationship module. The category and color modules assign respective category and color labels to region proposals, enabling the computation of category and color scores. Simultaneously, the spatial relationship module integrates spatial cues, yielding a spatial score for each proposal to enhance localization accuracy further. By adeptly amalgamating the category, color, and spatial scores, we derive a refined grounding score for every proposal. Comprehensive evaluations on the RefCOCO, RefCOCO+, and RefCOCOg datasets manifest the prowess of the Universal Relocalizer, showcasing its formidable performance across the board.

Despite the great success achieved, existing video moment retrieval (VMR) methods are developed under the assumption that data are centralizedly stored. However, in real-world applications, due to the inherent nature of data generation and privacy concerns, data are often distributed on different silos, bringing huge challenges to effective large-scale training. In this work, we try to overcome above limitation by leveraging the recent success of federated learning. As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data. Then, a novel federated learning method named FedVMR is proposed to facilitate large-scale and secure training of VMR models in decentralized environment. Experiments on benchmark datasets demonstrate its effectiveness. This work is the very first attempt to enable safe and efficient VMR training in decentralized scene, which is hoped to pave the way for further study in the related research field.

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.

The Vivid Talking Head Video Generation track of the "ACM Multimedia ViCo 2023 Conversational Head Generation Challenge'' aims to generate realistic face-to-face conversation videos based on audio and reference images. However, the direct synthesis of reference videos from audio and reference images poses a significant challenge. In response, we propose a comprehensive method that combines audio-driven 3DMM parameter prediction with a rendering network to generate high-fidelity lip-sync talking head videos. In the first stage, we leverage the audio input to predict the 3DMM parameters, capturing essential facial expressions and head poses needed for realistic video generation. In the second stage, we employ a sophisticated rendering network designed to ensure accurate lip synchronization and natural facial expressions. This network enhances the visual quality and realism of the generated videos. We are delighted to announce that our method achieved first place in the talking head generation track of the challenge and was honored with the People's Selection Award. The source code for our method is accessible1, allowing others to build upon our work and drive further advancements in the field of conversational head generation.

Video question answering is an increasingly vital research field, spurred by the rapid proliferation of video content online and the urgent need for intelligent systems that can comprehend and interact with this content. Existing methodologies often lean towards video understanding and cross-modal information interaction modeling but tend to overlook the crucial aspect of comprehensive question understanding. To address this gap, we introduce the multi-modal and multi-layer question enhancement network, a groundbreaking framework emphasizing nuanced question understanding. Our approach begins by extracting object, appearance, and motion features from videos. Subsequently, we harness multi-layer outputs from a pre-trained language model, ensuring a thorough grasp of the question. Integrating object data into appearance is guided by global question and frame representation, facilitating the adaptive acquisition of appearance and motion-enhanced question representation. By amalgamating multi-modal question insights, our methodology adeptly determines answers to questions. Experimental results conducted on three benchmarks demonstrate the superiority of our tailored approach, underscoring the importance of advanced question comprehension in VideoQA.

Emotion recognition in conversation aims to identify the emotions underlying each utterance, and it has great potential in various domains. Human perception of emotions relies on multiple modalities, such as language, vocal tonality, and facial expressions. While many studies have incorporated multimodal information to enhance emotion recognition, the performance of multimodal models often plateaus when additional modalities are added. We demonstrate through experiments that the main reason for this plateau is an imbalanced assignment of gradients across modalities. To address this issue, we propose fine-grained adaptive gradient modulation, a plug-in approach to rebalance the gradients of modalities. Experimental results show that our method improves the performance of all baseline models and outperforms existing plug-in methods.

Multimodal sentence summarization, aiming to generate a brief summary of the source sentence and image, is a new yet challenging task. Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. To be specific, we propose a prompt-guided image encoding module to extract the source image feature. It leverages several soft to-be-learned prompts for image patch embedding, which facilitates the visual content injection to BART for open-domain MMSS tasks. Thereafter, we devise an explicit source critical token learning module to directly capture the critical tokens of the source sentence with the reference of the source image, where we incorporate explicit supervision to improve performance. Extensive experiments on a public dataset fully validate the superiority of our proposed method. In addition, the predicted tokens by the vision-guided key-token highlighting module can be easily understood by humans and hence improve the interpretability of our model.

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but ignores the higher-order neighbor relations (i.e., a matching structure among multiple data samples). Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. However, it is ineffective to directly extend existing re-ranking algorithms to image-text retrieval. In this paper, we analyze the reason from four perspectives, i.e., generalization, flexibility, sparsity, and asymmetry, and propose a novel learnable pillar-based re-ranking paradigm. Concretely, we first select top-ranked intra- and intermodal neighbors as pillars, and then reconstruct data samples with the neighbor relations between them and the pillars. In this way, each sample can be mapped into a multimodal pillar space only using similarities, ensuring generalization. After that, we design a neighbor-aware graph reasoning module to flexibly exploit the relations and excavate the sparse positive items within a neighborhood. We also present a structure alignment constraint to promote crossmodal collaboration and align the asymmetric modalities. On top of various base backbones, we carry out extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, demonstrating the effectiveness, superiority, generalization, and transferability of our proposed re-ranking paradigm.

Partial person re-identification (ReID) aims to solve the problem of image spatial misalignment due to occlusions or out-of-views. Despite significant progress through the introduction of additional information, such as human pose landmarks, mask maps, and spatial information, partial person ReID remains challenging due to noisy keypoints and impressionable pedestrian representations. To address these issues, we propose a unified attribute-guided collaborative learning scheme for partial person ReID. Specifically, we introduce an adaptive threshold-guided masked graph convolutional network that can dynamically remove untrustworthy edges to suppress the diffusion of noisy keypoints. Furthermore, we incorporate human attributes and devise a cyclic heterogeneous graph convolutional network to effectively fuse cross-modal pedestrian information through intra- and inter-graph interaction, resulting in robust pedestrian representations. Finally, to enhance keypoint representation learning, we design a novel part-based similarity constraint based on the axisymmetric characteristic of the human body. Extensive experiments on multiple public datasets have shown that our model achieves superior performance compared to other state-of-the-art baselines.

Video question answering, aiming to answer a natural language question related to the given video, has gained popularity in the last few years. Although significant improvements have been achieved, it is still confronted with two challenges: the sufficient comprehension of video content and the long-tailed answers. To this end, we propose a multi-granularity interaction and integration network for video question answering. It jointly explores multi-level intra-granularity and inter-granularity relations to enhance the comprehension of videos. To be specific, we first build a word-enhanced visual representation module to achieve cross-modal alignment. And then we advance a multi-granularity interaction module to explore the intra-granularity and inter-granularity relationships. Finally, a question-guided interaction module is developed to select question-related visual representations for answer prediction. In addition, we employ the seesaw loss for open-ended tasks to alleviate the long-tailed word distribution effect. Both the quantitative and qualitative results on TGIF-QA, MSRVTT-QA, and MSVD-QA datasets demonstrate the superiority of our model over several state-of-the-art approaches.

Talking face generation is the process of synthesizing a lip-synchronized video when given a reference portrait and an audio clip. However, generating a fine-grained talking video is nontrivial due to several challenges: 1) capturing vivid facial expressions, such as muscle movements; 2) ensuring smooth transitions between consecutive frames; and 3) preserving the details of the reference portrait. Existing efforts have only focused on modeling rigid lip movements, resulting in low-fidelity videos with jerky facial muscle deformations. To address these challenges, we propose a novel Fine-gRained mOtioN moDel (FROND), consisting of three components. In the first component, we adopt a two-stream encoder to capture local facial movement keypoints and embed their overall motion context as the global code. In the second component, we design a motion estimation module to predict audio-driven movements. This enables the learning of local key point motion in the continuous trajectory space to achieve smooth temporal facial movements. Additionally, the local and global motions are fused to estimate a continuous dense motion field, resulting in spatially smooth movements. In the third component, we devise a novel implicit image decoder based on an implicit neural network. This decoder recovers high-frequency information from the input image, resulting in a high-fidelity talking face. In summary, the FROND refines the motion trajectories of facial keypoints into a continuous dense motion field, which is followed by a decoder that fully exploits the inherent smoothness of the motion. We conduct quantitative and qualitative model evaluations on benchmark datasets. The experimental results show that our proposed FROND significantly outperforms several state-of-the-art baselines.

Scene Graph Generation (SGG) is to abstract the objects and their semantic relationships within a given image. Current SGG performance is mainly limited by the biased predicate prediction caused by the long-tailed data distribution. Though many unbiased SGG methods have emerged to enhance the prediction of the tail predicates, their improvements on the tail predicates are often accompanied by the deterioration on the head ones, leading the prediction overly debiased. Toward this end, in this work, we propose a Dual-Biased Predicate Predictor (DBiased-P) to boost the unbiased SGG, which comprises a re-weighted primary classifier and an unweighted auxiliary classifier. The former classifier is tail-biased and used for the final predicate prediction, while the latter one is head-biased and designed to boost the head predicate prediction of the primary classifier by a head-oriented soft regularization. Experiments conducted on Visual Genome and Open Image datasets indicate the superiority of our DBiased-P in unbiased SGG, which significantly improves the recall@50 of the state-of-the-art unbiased SGG method DT2-ACBS from 23.3% to 55.5% as well as the mean recall@50 from 35.9% to 37.7%.

Video moment retrieval, i.e., localizing the specific video moments within a video given a description query, has attracted substantial attention over the past several years. Although great progress has been achieved thus far, most of existing methods are supervised, which require moment-level temporal annotation information. In contrast, weakly-supervised methods which only need video-level annotations remain largely unexplored. In this paper, we propose a novel end-to-end Siamese alignment network for weakly-supervised video moment retrieval. To be specific, we design a multi-scale Siamese module, which could progressively reduce the semantic gap between the visual and textual modality with the Siamese structure. In addition, we present a context-aware multiple instance learning module by considering the influence of adjacent contexts, enhancing the moment-query and video-query alignment simultaneously. By promoting the matching of both moment-level and video-level, our model can effectively improve the retrieval performance, even if only having weak video level annotations. Extensive experiments on two benchmark datasets, i.e., ActivityNet-Captions and Charades-STA, verify the superiority of our model compared with several state-of-the-art baselines.

Multi-modal medical image fusion is a long-standing important research topic that can obtain informative medical images and assist doctors diagnose and treat diseases more efficiently. However, most fusion methods extract and fuse features by subjectively defining constraints, which easily distorts the unique information of source images. In this work, we present a novel end-to-end unsupervised network to fuse multi-modal medical images. It is composed of a generator and two symmetrical discriminators. The former aims to generate a ”real-like” fused image based on a specifically designed content and structure loss, while the latter are devoted to distinguishing the differences between the fused image and the source ones. They are trained alternately until discriminators cannot distinguish the fused image from the source ones. In addition, the symmetrical discriminator scheme is conducive to maintaining the feature consistency among different modalities. More importantly, to enhance the retention degree of texture details, U-Net is adopted as the generator heuristically, where the up-sampling method is modified to bilinear interpolation for avoiding checkerboard artifacts. As for the optimization, we define the content loss function, which preserves the gradient information and pixel activity of source images. Both visual analysis and quantitative evaluation of experimental results show the superiority of our method as compared to the cutting-edge baselines.

To date, only a few works have focused on the cloth-changing person Re-identification (ReID) task, but since it is very difficult to extract generalized and robust features for representing people with different clothes, thus, their performances need to be improved. Moreover, visual-semantic information is also often ignored. To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of cloth-changing. Specifically, to fully represent a person with clothing changes, a multigranular feature representation scheme (MGR) is employed to adaptively extract multilevel and multigranular feature information, and then a cloth desensitization network (CDN) is designed to improve the feature robustness for the person with different clothes, where different high-level human attributes are fully utilized. Moreover, to further solve the issue of pose changes and occlusion under different camera perspectives, a partially semantically aligned network (PSA) is proposed to obtain the visual-semantic information that is used to align the human attributes. Most importantly, these three modules are jointly explored in a unified framework. Extensive experimental results on four cloth-changing person ReID datasets demonstrate that the MVSE algorithm can extract highly robust feature representations of cloth-changing persons, and it can outperform state-of-the-art cloth-changing person ReID approaches.

Personalized outfit recommendation, which aims to recommend the outfits to a given user according to his/her preference, has gained increasing research attention due to its economic value. Nevertheless, the majority of existing methods mainly focus on improving the recommendation effectiveness, while overlooking the recommendation efficiency. Inspired by this, we devise a novel bi-directional heterogeneous graph hashing scheme, called BiHGH, towards efficient personalized outfit recommendation. In particular, this scheme consists of three key components: heterogeneous graph node initialization, bi-directional sequential graph convolution, and hash code learning. We first unify four types of entities (i.e., users, outfits, items, and attributes) and their relations via a heterogeneous four-partite graph. To perform graph learning, we then creatively devise a bi-directional graph convolution algorithm to sequentially transfer knowledge via repeating upwards and downwards convolution, whereby we divide the four-partite graph into three subgraphs and each subgraph only involves two adjacent entity types. We ultimately adopt the bayesian personalized ranking loss for the user preference learning and design the dual similarity preserving regularization to prevent the information loss during hash learning. Extensive experiments on the benchmark dataset demonstrate the superiority of BiHGH.

ViCo conversational head generation challenge is organized in conjunction with ACM Multimedia 2022. The challenge includes two tasks: 1) generating a vivid talking head according to a reference image and driving audio, and 2) synthesizing a listening head conditioned on the reference image and speaker's behaviors. For talking head generation, we design a three-stage method to generate high-quality and lip-sync talking head videos with natural head poses, which wins 3rd place and People's Selection Award. On the listening head generation track, we adopt MakeItTalk to generate vivid head movements of listeners, which achieves 2nd place in the competition.

VCIP 2022 “Tire pattern image classification based on lightweight network challenge” aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on ImageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.

Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for 3-D object tracking in point clouds. Nevertheless, existing methods mainly resort to developing a powerful classifier based on the initial bounding box seeds. In this article, we propose an end-to-end deep supervised descent method (SDM), which seamlessly integrates multiple seeds generation for the initialization of seeds and sequential updates for the estimation of accurate result. Specifically, we start with transforming the SDM iterative process into a trainable recurrent module. It explicitly learns a series of descent directions in the parameter space, to gradually optimize the initial seeds. Moreover, to alleviate drifting of this process, we initialize multiple seeds based on aggregated point sets generated by the deep Hough voting. Besides, a discrimination module is introduced to determine the bounding box with the highest score as the final result. Importantly, a specific multitask loss is proposed to train our model in an end-to-end way. Experiments on KITTI, PandaSet, and Waymo datasets show that our method could achieve significant improvements (up to 11.2% in success ratio) as compared to state-of-the-art trackers.

Video moment retrieval targets at retrieving a golden moment in a video for a given natural language query. The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream, and 2) bridging the semantic gap between textual query and video contents. To tackle those problems, early approaches adopt the sliding window or uniform sampling to collect video clips first and then match each clip with the query to identify relevant clips. Obviously, these strategies are time-consuming and often lead to unsatisfied accuracy in localization due to the unpredictable length of the golden moment. To avoid the limitations, researchers recently attempt to directly predict the relevant moment boundaries without the requirement to generate video clips first. One mainstream approach is to generate a multimodal feature vector for the target query and video frames (e.g., concatenation) and then use a regression approach upon the multimodal feature vector for boundary detection. Although some progress has been achieved by this approach, we argue that those methods have not well captured the cross-modal interactions between the query and video frames. In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal boundaries based on an interaction modeling between two modalities. In addition, an attention module is introduced to automatically assign higher weights to query words with richer semantic cues, which are considered to be more important for finding relevant video contents. Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy. Extensive experiments on two public datasets TACoS and Charades-STA demonstrate the superiority of our method over several state-of-the-art methods. Ablation studies have been also conducted to examine the effectiveness of different modules in our ACRM model.

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and sentence is intuitive and efficient for understanding semantics but is rarely considered.Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.

In industrial enterprises, effective retrieval of three-dimensional (3-D) computer-aided design (CAD) models can greatly save time and cost in new product development and manufacturing, thus, many researchers have focused on it. Recently, many view-based 3D model retrieval methods have been proposed and have achieved state-of-the-art performance. However, most of these methods focus on extracting more discriminative view-level features and effectively aggregating the multi-view images of a 3D model, and the latent relationship among these multi-view images is not fully explored. Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images. To capture associations among views, in this work, we propose a novel patch convolutional neural network (PCNN ) for view-based 3D model retrieval. Specifically, we first employ a CNN to extract patch features of each view image separately. Second, a novel neural network module named PatchConv is designed to exploit intrinsic relationships between neighboring patches in the feature space to capture long-range associations among multi-view images. Then, an adaptive weighted view layer is further embedded into PCNN to automatically assign a weight to each view according to the similarity between each view feature and the view-pooling feature. Finally, a discrimination loss function is employed to extract the discriminative 3D model feature, which consists of softmax loss values generated by the fusion classifier and the specific classifier. Extensive experimental results on two public 3D model retrieval benchmarks, namely, the ModelNet40, and ModelNet10, demonstrate that our proposed PCNN can outperform state-of-the-art approaches, with mAP values of 93.67%, and 96.23%, respectively.

Visual Question Answering (VQA) is a vital yet challenging task in the field of multimedia comprehension. In order to correctly answer questions about an image, a VQA model requires to sufficiently understand the visual scene, especially the vision-semantic reasonings between the two modalities. Traditional relation-based methods allow to encode the pairwise relations of objects to boost the VQA model performance. However, this simple strategy is deficient to exploit the abundant concepts expressed by the composition of diverse image objects, leading to sub-optimal performance. In this paper, we propose a focal and composed vision-semantic modeling method, which is a trainable end-to-end model, for better vision-semantic redundancy removal and compositionality modeling. Concretely, we first introduce the LENA cell, a plug-and-play reasoning module, which removes redundant semantic by a focal mechanism in the first step, followed by the vision-semantic compositionality modeling for better visual reasoning. We then incorporate the cell into a full LENA network, which progressively refines multimodal composed representations, and can be leveraged to infer the high-order vision-semantic in a multi-step learning way. Extensive experiments on two benchmark datasets, i.e., VQA v2 and VQA-CP v2, verify the superiority of our model as compared with several state-of-the-art baselines.

Multimodal dialog system has attracted increasing attention from both academia and industry over recent years. Although existing methods have achieved some progress, they are still confronted with challenges in the aspect of question understanding (i.e., user intention comprehension). In this paper, we present a relational graph-based context-aware question understanding scheme, which enhances the user intention comprehension from local to global. Specifically, we first utilize multiple attribute matrices as the guidance information to fully exploit the product-related keywords from each textual sentence, strengthening the local representation of user intentions. Afterwards, we design a sparse graph attention network to adaptively aggregate effective context information for each utterance, completely understanding the user intentions from a global perspective. Moreover, extensive experiments over a benchmark dataset show the superiority of our model compared with several state-of-the-art baselines.

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although much progress has been made in bridging vision and language, it remains challenging because of the difficult intra-modal reasoning and cross-modal alignment. Existing modality interaction methods have achieved impressive results on public datasets. However, they heavily rely on expert experience and empirical feedback towards the design of interaction patterns, therefore, lacking flexibility. To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To endow the model with the capability of path decision, we integrate a dynamic router in each cell for pattern exploration. As the routers are conditioned on inputs, our model can dynamically learn different activated paths for different data. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, verify the superiority of our model compared with several state-of-the-art baselines.

Due to the continuous booming of surveillance and Web videos, video moment localization, as an important branch of video content analysis, has attracted wide attention from both industry and academia in recent years. It is, however, a non-trivial task due to the following challenges: temporal context modeling, intelligent moment candidate generation, as well as the necessary efficiency and scalability in practice. To address these impediments, we present a deep end-to-end cross-modal hashing network. To be specific, we first design a video encoder relying on a bidirectional temporal convolutional network to simultaneously generate moment candidates and learn their representations. Considering that the video encoder characterizes temporal contextual structures at multiple scales of time windows, we can thus obtain enhanced moment representations. As a counterpart, we design an independent query encoder towards user intention understanding. Thereafter, a cross-model hashing module is developed to project these two heterogeneous representations into a shared isomorphic Hamming space for compact hash code learning. After that, we can effectively estimate the relevance score of each "moment-query" pair via the Hamming distance. Besides effectiveness, our model is far more efficient and scalable since the hash codes of videos can be learned offline. Experimental results on real-world datasets have justified the superiority of our model over several state-of-the-art competitors.

Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors.

This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. We introduce two novel mechanisms: an Alternate Attention-Transfer Mechanism (AATM) and a Semantic Distillation Mechanism (SDM), to help generator better bridge the cross-domain gap between text and image. The AATM updates word attention weights and attention weights of image sub-regions alternately, to progressively highlight important word information and enrich details of synthesized images. The SDM uses the image encoder trained in the Image-to-Image task to guide training of the text encoder in the Text-to-Image task, for generating better text features and higher-quality images. With extensive experimental validation on two public datasets, our KT-GAN outperforms the baseline method significantly, and also achieves the competive results over different evaluation metrics.

Designing an effective and uniform framework to meliorate tracking performance is very meaningful and essential. However, existing methods merely focus on single positive and negative instances corresponding to the exemplar, thoroughly ignoring the effective information hidden in other instances. To tackle this issue, in this paper, we present an informative enhanced loss based Siamese tracking network. Specifically, we introduce an informative enhanced loss to enable the network to capture information from an overall perspective. In other words, we construct dense connections among instances and exemplar. More importantly, we prove that our proposed loss can be transformed into the logistic loss and the triplet loss under particular parameter settings. Experiments on prevalent benchmarks demonstrate that the Siamese frameworks trained with our proposed loss indeed obtain better tracking results than original ones, and achieve promising performance against several state-of-the-art trackers on the real-time challenge.

In this article, we tackle the cross-modal video moment localization issue, namely, localizing the most relevant video moment in an untrimmed video given a sentence as the query. The majority of existing methods focus on generating video moment candidates with the help of multi-scale sliding window segmentation. They hence inevitably suffer from numerous candidates, which result in the less effective retrieval process. In addition, the spatial scene tracking is crucial for realizing the video moment localization process, but it is rarely considered in traditional techniques. To this end, we innovatively contribute a spatial-temporal reinforcement learning framework. Specifically, we first exploit a temporal-level reinforcement learning to dynamically adjust the boundary of localized video moment instead of the traditional window segmentation strategy, which is able to accelerate the localization process. Thereafter, a spatial-level reinforcement learning is proposed to track the scene on consecutive image frames, therefore filtering out less relevant information. Lastly, an alternative optimization strategy is proposed to jointly optimize the temporal- and spatial-level reinforcement learning. Thereinto, the two tasks of temporal boundary localization and spatial scene tracking are mutually reinforced. By experimenting on two real-world datasets, we demonstrate the effectiveness and rationality of our proposed solution.

Image-text matching is a vital yet challenging task in the field of multimedia analysis. Over the past decades, great efforts have been made to bridge the semantic gap between the visual and textual modalities. Despite the significance and value, most prior work is still confronted with a multi-view description challenge, i.e., how to align an image to multiple textual descriptions with semantic diversity. Toward this end, we present a novel context-aware multi-view summarization network to summarize context-enhanced visual region information from multiple views. To be more specific, we design an adaptive gating self-attention module to extract representations of visual regions and words. By controlling the internal information flow, we are able to adaptively capture context information. Afterwards, we introduce a summarization module with a diversity regularization to aggregate region-level features into image-level ones from different perspectives. Ultimately, we devise a multi-view matching scheme to match multi-view image features with corresponding text ones. To justify our work, we have conducted extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, which demonstrates the superiority of our model as compared to several state-of-the-art baselines.

Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.

Video person re-identification (video Re-ID) plays an important role in surveillance video analysis and has gained increasing attention recently. However, existing supervised methods require vast labeled identities across cameras, resulting in poor scalability in practical applications. Although some unsupervised approaches have been exploited for video Re-ID, they are still in their infancy due to the complex nature of learning discriminative features on unlabelled data. In this paper, we focus on one-shot video Re-ID and present an iterative local-global collaboration learning approach to learning robust and discriminative person representations. Specifically, it jointly considers the global video information and local frame sequence information to better capture the diverse appearance of the person for feature learning and pseudo-label estimation. Moreover, as the cross-entropy loss may induce the model to focus on identity-irrelevant factors, we introduce the variational information bottleneck as a regularization term to train the model together. It can help filter undesirable information and characterize subtle differences among persons. Since accuracy cannot always be guaranteed for pseudo-labels, we adopt a dynamic selection strategy to select part of pseudo-labeled data with higher confidence to update the training set and re-train the learning model. During training, our method iteratively executes the feature learning, pseudo-label estimation, and dynamic sample selection until all the unlabeled data have been seen. Extensive experiments on two public datasets, i.e., DukeMTMC-VideoReID and MARS, have verified the superiority of our model to several cutting-edge competitors.

Hashtags, a user provides to a micro-video, are the ones which can well describe the semantics of the micro-video's content in his/her mind. At the same time, hashtags have been widely used to facilitate various micro-video retrieval scenarios (e.g., search, browse, and categorization). Despite their importance, numerous micro-videos lack hashtags or contain inaccurate or incomplete hashtags. In light of this, hashtag recommendation, which suggests a list of hashtags to a user when he/she wants to annotate a post, becomes a crucial research problem. However, little attention has been paid to micro-video hashtag recommendation, mainly due to the following three reasons: 1) lack of benchmark dataset; 2) the temporal and multi-modality characteristics of micro-videos; and 3) hashtag sparsity and long-tail distributions. In this paper, we recommend hashtags for micro-videos by presenting a novel multi-view representation interactive embedding model with graph-based information propagation. It is capable of boosting the performance of micro-videos hashtag recommendation by jointly considering the sequential feature learning, the video-user-hashtag interaction, and the hashtag correlations. Extensive experiments on a constructed dataset demonstrate our proposed method outperforms state-of-the-art baselines. As a side research contribution, we have released our dataset and codes to facilitate the research in this community.

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism. We release the codes and datasets at https://github.com/xiangwang1223/knowledge_graph_attention_network.

What made you want to wear the clothes you are wearing? Where is the place you want to visit for your next-coming holiday? Why do you like the music you frequently listen to? If you are like most people, you probably made these decisions as a result of watching influencers on social media. Furthermore, influencer marketing is an opportunity for brands to take advantage of social media using a well-defined and well-designed social media marketing strategy. However, choosing the right influencers is not an easy task. With more people gaining an increasing number of followers in social media, finding the right influencer for an E-commerce company becomes paramount. In fact, most marketers cite it as a top challenge for their brands. To address the aforementioned issues, we proposed a data-driven micro-influencer ranking scheme to solve the essential question of finding out the right micro-influencer. Specifically, we represented brands and influencers by fusing their historical posts' visual and textual information. A novel k-buckets sampling strategy with a modified listwise learning to rank model were proposed to learn a brand-micro-influncer scoring function. In addition, we developed a new Instagram brand micro-influencer dataset, consisting of 360 brands and 3,748 micro-influencers, which can benefit future researchers in this area. The extensive evaluations demonstrate the advantage of our proposed method compared with the state-of-the-art methods.

In order to help people develop healthy eating habits, we present a personalized health-aware food recommendation system, calledMarket2Dish. Market2Dish could recognize the ingredients in the micro-videos taken from the market, characterize the health conditions of users from their social media accounts, and ultimately recommend users with the personalized healthy foods. Specifically, we employ a word-class interaction based text classification model to learn the fine-grained similarity between sparse health features on the social media platforms and pre-defined health concepts, and then a category-aware hierarchical memory network based recommender is introduced to learn the user-recipe interactions for better food recommendations. Moreover, we demonstrate this system as an online app for real-time interactions with users.

In the past few years, micro-videos have become the dominant trend in the social media era. Meanwhile, as the number of microvideos increases, users are frequently overwhelmed by their uninterested ones. Despite the success of existing recommendation systems developed for various communities, they cannot be applied to routing micro-videos, since users in micro-video platforms have their unique characteristics: diverse and dynamic interest, multilevel interest, as well as true negative samples. To address these problems, we present a temporal graph-guided recommendation system. In particular, we first design a novel graph-based sequential network to simultaneously model users' dynamic and diverse interest.Similarly, uninterested information can be captured from users'true negative samples. Beyond that, we introduce users' multi-level interest into our recommendation model via a user matrix that is able to learn the enhanced representation of users' interest. Finally, the system can make accurate recommendation by considering the above characteristics. Experimental results on two public datasets verify the effectiveness of our proposed model.

Micro-videos have rapidly become one of the most dominant trends in the era of social media. Accordingly, how to organize them draws our attention. Distinct from the traditional long videos that would have multi-site scenes and tolerate the hysteresis, a micro-video: 1) usually records contents at one specific venue within a few seconds. The venues are structured hierarchically regarding their category granularity. This motivates us to organize the micro-videos via their venue structure. 2) timely circulates over social networks. Thus, the timeliness of micro-videos desires effective online processing. However, only 1.22% of micro-videos are labeled with venue information when uploaded at the mobile end. To address this problem, we present a framework to organize the micro-videos online. In particular, we first build a structure-guided multi-modal dictionary learning model to learn the concept-level micro-video representation by jointly considering their venue structure and modality relatedness. We then develop an online learning algorithm to incrementally and efficiently strengthen our model, as well as categorize the micro-videos into a tree structure. Extensive experiments on a real-world data set validate our model well. In addition, we have released the codes to facilitate the research in the community.

In this paper, we address the temporal moment localization issue, namely, localizing a video moment described by a natural language query in an untrimmed video. This is a general yet challenging vision-language task since it requires not only the localization of moments, but also the multimodal comprehension of textual-temporal information (e.g., "first" and "leaving") that helps to distinguish the desired moment from the others, especially those with the similar visual content. While existing studies treat a given language query as a single unit, we propose to decompose it into two components: the relevant cue related to the desired moment localization and the irrelevant one meaningless to the localization. This allows us to flexibly adapt to arbitrary queries in an end-to-end framework. In our proposed model, a language-temporal attention network is utilized to learn the word attention based on the temporal context information in the video. Therefore, our model can automatically select "what words to listen to" for localizing the desired moment. We evaluate the proposed model on two public benchmark datasets: DiDeMo and Charades-STA. The experimental results verify its superiority over several state-of-the-art methods.

Recently, Image-to-Image Translation (IIT) has achieved great progress in image style transfer and semantic context manipulation for images. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to adapt to a new domain. To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images in sparsely grouped datasets where only a few train samples are labelled. Using a one-input multi-output architecture, SG-GAN is well-suited for tackling multi-task learning and sparsely grouped learning tasks. The new model is able to translate images among multiple groups using only a single trained model. To experimentally validate the advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images as a case study. Experimental results show that SG-GAN can achieve comparable results with state-of-the-art methods on adequately labelled datasets while attaining a superior image translation quality on sparsely grouped datasets~\footnoteCode is available at https://github.com/zhangqianhui/SGGAN-tensorflow..

In the past few years, language-based video retrieval has attracted a lot of attention. However, as a natural extension, localizing the specific video moments within a video given a description query is seldom explored. Although these two tasks look similar, the latter is more challenging due to two main reasons: 1) The former task only needs to judge whether the query occurs in a video and returns an entire video, but the latter is expected to judge which moment within a video matches the query and accurately returns the start and end points of the moment. Due to the fact that different moments in a video have varying durations and diverse spatial-temporal characteristics, uncovering the underlying moments is highly challenging. 2) As for the key component of relevance estimation, the former usually embeds a video and the query into a common space to compute the relevance score. However, the later task concerns moment localization where not only the features of a specific moment matter, but the context information of the moment also contributes a lot. For example, the query may contain temporal constraint words, such as "first'', therefore need temporal context to properly comprehend them. To address these issues, we develop an Attentive Cross-Modal Retrieval Network. In particular, we design a memory attention mechanism to emphasize the visual features mentioned in the query and simultaneously incorporate their context. In the light of this, we obtain the augmented moment representation. Meanwhile, a cross-modal fusion sub-network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. We evaluate our method on two datasets: DiDeMo and TACoS. Extensive experiments show the effectiveness of our model as compared to the state-of-the-art methods.

Like the traditional long videos, micro-videos are the unity of textual, acoustic, and visual modalities. These modalities sequentially tell a real-life event from distinct angles. Yet, unlike the traditional long videos with rich content, micro-videos are very short, lasting for 6-15 seconds, and they hence usually convey one or a few high-level concepts. In the light of this, we have to characterize and jointly model the sparseness and multiple sequential structures for better micro-video understanding. To accomplish this, in this paper, we present an end-to-end deep learning model, which packs three parallel LSTMs to capture the sequential structures and a convolutional neural network to learn the sparse concept-level representations of micro-videos. We applied our model to the application of micro-video categorization. Besides, we constructed a real-world dataset for sequence modeling and released it to facilitate other researchers. Experimental results demonstrate that our model yields better performance than several state-of-the-art baselines.

To fairly allocate network bandwidth for different streams and to achieve a smooth quality between different video sequences are two key issues in multi-stream applications. In this paper, a new joint rate control scheme for multiple video streams based on game theory is proposed. We achieve a fair rate allocation in video quality between different sequences by Nash Bargaining theory under the constraints of buffer and bandwidth. Also, by grouping all the different sequences' frames within the same timeslot into a coalition, we can avoid the fluctuation in a sequence by bargaining between current coalition and remaining coalitions. Some experiments have been conducted to verify the proposed method.

Motion information occupies a considerable portion of the bit stream generated by the state-of-the-art video codecs. As a sequence, reducing bits on motion information can improve the overall performance of video compression. Various competition based motion vector coding (MVC) schemes were proposed for this goal. In this paper we propose a region based motion vector prediction (RMVP) algorithm to save signaling bits in conventional MVC schemes. Specifically, the motion vector predictor selection information is embedded into the parity of the motion vector differences which would be transmitted into the decoder. To obtain the modulated motion vector differences, the encoder needs to divide the reference frame into several regions and then perform constrained motion estimation in some of the regions. To confirm the advantages of RMVP over different MVC schemes, we implemented it on both KTA Software and TMuC test model. Experimental results show that RMVP outperforms existing MVC schemes.

In this paper, we present a novel low complexity yet very efficient rate control algorithm for Scalable Video Coding (SVC) based on the ρ-domain model. Compared with the conventional ρ-domain model in determining quantization parameter, the proposed algorithm adopts a new linear model to obtain the quantization parameter at frame-level. This linear model is able to characterize the relationship between the percentage of zero among quantized coefficients and the quantization step. Since this model considers the Mean Absolute Difference (MAD) of each frame, individual frame only needs to be encoded once to control the bit-rate. In particular, the model parameter in the ρ-domain model can be adaptively estimated by utilizing either temporal or inter-layer information. This leads to significant improvement in estimation accuracy. Experimental results show that the proposed algorithm can achieve accurate bit-rate and maintain the maximum mismatch between actual bit-rate and target bit-rate within 0.5%. More importantly, the proposed low complexity algorithm is able to obtain PSNR improvement up to 0.6dB comparing with the algorithm adopted in the Joint Scalable Video Model (JSVM).

Video adaptation has been proved to be an efficient technique in dealing with various constraints such as bandwidth limitation and user requirement in multimedia applications. However, existing methods including Scalable Video Coding and transcoding cannot get a fine performance when bandwidth constraints exist in various scenarios particularly in realtime applications. In this paper, we propose a novel rate control scheme based on intermediate description. The proposed scheme can provide fast rate control for narrow and time-varying transmission channel in scenarios such as video streaming, video sharing and video on demand. In this scheme, Discrete Cosine Transform (DCT) coefficients distribution is modeled by generalized Gaussian distribution, meanwhile the parameter information of this model is stored as side information for rate control. With the stored parameter information, encoder and transcoder can achieve the target bit-rate with low complexity. Furthermore, an initial Quantization Parameter (QP) determination method is also presented to calculate a proper QP for the Instantaneous Decoding Refresh (IDR) picture. Experimental results show that compared with JVT-G012 in H.264, the proposed rate control scheme can save more than 85% encoding time and obtain the required bit-rate more precisely, meanwhile gains a performance improvement by 0.2dB averagely.