Automatic echocardiography video segmentation is a powerful tool for improving the accuracy of cardiovascular function assessment. However, it remains a challenging task owing to (1) extensive speckle noise and blurred boundaries, (2) dramatic shape variations of targeting structures across frames, and (3) limited labeled data due to the high cost of annotation. In this paper, we present a novel semi-supervised segmentation model based on Vision Mamba (Vim) to comprehensively tackle these challenges; we call it EchoVim. Our framework introduces three technical innovations: First, a bidirectional inference mechanism (BIM) which can propagate label information bidirectionally from end-diastolic (ED) and end-systolic (ES) frames to generate pseudo-labels, coupled with confidence-aware dynamic updating to progressively refine supervision signals. Second, a dynamic interaction temporal alignment (DITA) module that establishes anatomical correspondence across frames by adaptively enhancing features near temporally stable regions while suppressing motion-irrelevant artifacts, effectively addressing variations in cardiac shape. Third, a semantic token-attentive refinement (STR) module that constructs low-rank semantic tokens to encode cardiac structure priors, utilizing attention-guided nonlinear transformations to disentangle speckle noise from true anatomical patterns. We conduct extensive experiments on two benchmarking echocardiography video datasets: CAMUS and EchoNet-Dynamic, and the results demonstrate that our method outperforms existing state-of-the-art approaches with real-time inference. Codes are available at https://github.com/guojx2255/EchoVim.
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The reasoning segmentation task involves segmenting objects within an image by interpreting implicit user instructions. Despite significant advancements made by existing approaches, they remain constrained by low perceptual resolution, as visual encoders are typically pre-trained at lower resolutions. Furthermore, simply interpolating the positional embeddings of visual encoders to enhance perceptual resolution yields only marginal performance improvements while incurring substantial computational costs. To address this, we propose HRSeg, an efficient model with high-resolution fine-grained perception. It features two key innovations: High-Resolution Perception (HRP) and High-Resolution Enhancement (HRE). The HRP module processes high-resolution images through cropping, integrating local and global features for multi-granularity quality. The HRE module enhances mask features by integrating fine-grained information from high-resolution images, refining their alignment with text features for precise segmentation. Extensive ablation studies validate the effectiveness of our modules, while comprehensive experiments on multiple benchmark datasets demonstrate HRSeg's superior performance. Code will be available at https://github.com/WeihuangLin/HRSeg.
The domain gap between pretraining data (e.g., ImageNet, LUPerson) and downstream ReID datasets often leads to suboptimal performance when directly fine-tuning pretrained models. While existing methods attempt to bridge this gap by incorporating additional modalities (e.g., text, 3D data) or visual cues (e.g., pose, body masks), these approaches introduce two key limitations: (1) they may distract the model with irrelevant factors like background clutter or clothing variations, and (2) they inevitably increase computational overhead during inference. To address these issues, we propose the Weak Saliency Feedback Transformer (WSFFormer), inspired by the feedback mechanisms in biological visual systems. Unlike traditional one-way feature propagation, WSFFormer employs an adaptive feedback loop during training to enhance low-response regions, enabling the model to capture richer and more discriminative features. The WSFFormer introduces three key components: (1) The Lateral Feedback Module (LFM) mimics retinal lateral inhibition by adaptively suppressing high-response regions and amplifying weak discriminative features, forcing attention on subtle details; (2) The Progressive Feedback Module (PFM) refines feedback through deep-to-shallow closed-loop propagation, blending high-level semantics with spatial details; (3) The Feedback Sensitive Entropy Loss (FSE Loss) optimizes target-domain adaptation by quantifying divergence between forward and feedback-corrected features. Experiments on holistic/occluded ReID benchmarks show WSFFormer outperforms ViT/Swin-based SOTA methods without extra inference cost.
Semi-supervised learning (SSL) has achieved significant progress by leveraging both labeled data and unlabeled data. Existing SSL methods overlook a common real-world scenario when labeled data is extremely scarce, potentially as limited as a single labeled sample in the dataset. General SSL approaches struggle to train effectively from scratch under such constraints, while methods utilizing pre-trained models often fail to find an optimal balance between leveraging limited labeled data and abundant unlabeled data. To address this challenge, we propose Firstly Adapt, Then catEgorize (FATE), a novel SSL framework tailored for scenarios with extremely limited labeled data. At its core, the two-stage prompt tuning paradigm FATE exploits unlabeled data to compensate for scarce supervision signals, then transfers to downstream tasks. Concretely, FATE first adapts a pre-trained model to the feature distribution of downstream data using volumes of unlabeled samples in an unsupervised manner. It then applies an SSL method specifically designed for pre-trained models to complete the final classification task. FATE is designed to be compatible with both vision and vision-language pre-trained models. Extensive experiments demonstrate that FATE effectively mitigates challenges arising from the scarcity of labeled samples in SSL, achieving an average performance improvement of 33.74% across seven benchmarks compared to state-of-the-art SSL methods. Code is available at https://github.com/ganchi-huanggua/FATE.git.
We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets while providing compatibility with various input-output interfaces required across different task scenarios. Current systems cannot meet these requirements, as they typically utilize task-specific architecture trained on narrow data domains with limited semantic coverage. Our study addresses these limitations from two aspects: data and modeling. We first introduce an automatic data engine that enjoys significantly better scalability compared to previous human annotation or rule-based approaches. It has enabled us to create the largest dataset of its kind to date, comprising 270K image-text-mask triplets covering an unprecedented range of diverse semantic categories and attribute specifications. Based on this data foundation, we further propose a task unification paradigm that centers around referring expression segmentation. It effectively handles a wide range of vision-centric perception tasks, including classification, detection, segmentation, grounding, etc, using a single model without any task-specific heads. Combining these innovations on data and modeling, we present RemoteSAM, a foundation model that establishes new SoTA on several earth observation perception benchmarks, outperforming other foundation models such as Falcon, GeoChat, and LHRS-Bot with significantly higher efficiency. Models and data are publicly available at https://github.com/1e12Leon/RemoteSAM.
Video Question Answering (VideoQA) based on Large Language Models (LLMs) has shown potential in general video understanding but faces significant challenges when applied to the inherently complex domain of sports videos. In this work, we propose FineQuest, the first training-free framework that leverages dual-mode reasoning inspired by cognitive science: i) Reactive Reasoning for straightforward sports queries and ii) Deliberative Reasoning for more complex ones. To bridge the knowledge gap between general-purpose models and domain-specific sports understanding, FineQuest incorporates SSGraph, a multimodal sports knowledge scene graph spanning nine sports, which encodes both visual instances and domain-specific terminology to enhance reasoning accuracy. Furthermore, we introduce two new sports VideoQA benchmarks, Gym-QA and Diving-QA, derived from the FineGym and FineDiving datasets, enabling diverse and comprehensive evaluation. FineQuest achieves state-of-the-art performance on these benchmarks as well as the existing SPORTU dataset, while maintains strong general VideoQA capabilities.
Few-shot parameter-efficient tuning methods demonstrate promising potential for Vision-Language (V-L) models in downstream tasks. However, existing approaches primarily focus on class-level alignment between image and text features, overlooking crucial instance-specific semantic information. This limitation leads to suboptimal performance on challenging tasks and restricted generalization capability to unseen data. To address these issues, we propose Class- and Instance-aware Adaptation (CIA), a novel framework that simultaneously optimizes both class-level and instance-level alignments. Specifically, CIA introduces a novel instance encoder that leverages cross-modal self-attention to generate instance-specific text features, accompanied by a carefully designed regularization mechanism to maintain consistency between class-level and instance-level representations. Extensive experiments across 15 benchmark datasets demonstrate that CIA significantly improves the downstream adaptation of V-L models.
Text-video retrieval (TVR) systems often suffer from visual-linguistic biases present in datasets, which cause pre-trained vision-language models to overlook key details. To address this, we propose BiMa, a novel framework designed to mitigate biases in both visual and textual representations. Our approach begins by generating scene elements that characterize each video by identifying relevant entities/objects and activities. For visual debiasing, we integrate these scene elements into the video embeddings, enhancing them to emphasize fine-grained and salient details. For textual debiasing, we introduce a mechanism to disentangle text features into content and bias components, enabling the model to focus on meaningful content while separately handling biased information. Extensive experiments and ablation studies across five major TVR benchmarks (i.e., MSR-VTT, MSVD, LSMDC, ActivityNet, and DiDeMo) demonstrate the competitive performance of BiMa. Additionally, the model's bias mitigation capability is consistently validated by its strong results on out-of-distribution retrieval tasks.
The objective of video frame interpolation (VFI) methods is to enhance video fluency and visual quality by generating intermediate frames between consecutive original frames based on the source video. Recently, diffusion-based VFI methods have made promising progresses, with generated results performing well in perceptual quality. However, these methods have not fully explored how to effectively leverage external motion priors to enhance the model's ability to estimate motion information between adjacent frames, which is crucial for VFI models to avoid generating blurry results due to the motion ambiguity. In this paper, we propose an Enhanced Motion-Aware latent Diffusion model ( EMADiff ) for video frame interpolation. Specifically, we integrate motion priors into the decoder of vector-quantized enhanced motion-aware GAN to guide the information propagation during RGB interpolated frame reconstruction. Furthermore, we propose enhanced motion-aware noising and de-noising procedures. By reducing the discrepancy in attention to motion priors between the forward and reverse processes, our EMADiff effectively utilizes motion priors, alleviates motion ambiguity, and generates realistic content. Comprehensive experiments on benchmark datasets show EMADiff achieves state-of-the-art performance, surpassing existing approaches and producing visually plausible and content-clear results.
While Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge, conventional single-agent RAG remains fundamentally limited in resolving complex queries demanding coordinated reasoning across heterogeneous data ecosystems. We present HM-RAG, a novel Hierarchical Multi-agent Multimodal RAG framework that pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data. The framework is composed of a three-tiered architecture with specialized agents: a Decomposition Agent that dissects complex queries into contextually coherent sub-tasks via semantic-aware query rewriting and schema-guided context augmentation; Multi-source Retrieval Agents that carry out parallel, modality-specific retrieval using plug-and-play modules designed for vector, graph, and web-based databases; and a Decision Agent that uses consistency voting to integrate multi-source answers and resolve discrepancies in retrieval results through Expert Model Refinement. This architecture attains comprehensive query understanding by combining textual, graph-relational, and web-derived evidence, resulting in a remarkable 12.95% improvement in answer accuracy and a 3.56% boost in question classification accuracy over baseline RAG systems on the ScienceQA and CrisisMMD benchmarks. Notably, HM-RAG establishes state-of-the-art results in zero-shot settings on both datasets. Its modular architecture ensures seamless integration of new data modalities while maintaining strict data governance, marking a significant advancement in addressing the critical challenges of multimodal reasoning and knowledge synthesis in RAG systems.
Driven by the increasing demand for applications in augmented and virtual reality, egocentric action recognition has emerged as a prominent research area. It is typically divided into two subtasks: recognizing the performed behavior (i.e., verb component) and identifying the objects being acted upon (i.e., noun component) from the first-person perspective. However, most existing approaches treat these two components as independent classification tasks, focusing on extracting component-specific knowledge while overlooking their inherent semantic and contextual relationships, leading to fragmented representations and sub-optimal generalization capability. To address these challenges, we propose a prompt learning-based framework, EgoPrompt , to conduct the egocentric action recognition task. Building on the existing prompting strategy to capture the component-specific knowledge, we construct a Unified Prompt Pool space to establish interaction between the two types of component representations. Specifically, the component representations (from verbs and nouns) are first decomposed into fine-grained patterns with the prompt pair form. Then, these pattern-level representations are fused through an attention-based mechanism to facilitate cross-component interaction. To ensure the prompt pool is informative, we further introduce a novel training objective, Diverse Pool Criteria . This objective realizes our goals from two perspectives: Prompt Selection Frequency Regularization and Prompt Knowledge Orthogonalization. Extensive experiments are conducted on the Ego4D, EPIC-Kitchens, and EGTEA datasets. The results consistently show that EgoPrompt achieves state-of-the-art performance across within-dataset, cross-dataset, and base-to-novel generalization benchmarks.
Learning from large-scale pre-trained models with strong generalization ability has shown remarkable success in a wide range of downstream tasks recently, but it is still underexplored in the challenging few-shot class-incremental learning (FSCIL) task. It aims to continually learn new concepts from limited training samples without forgetting the old ones at the same time. In this paper, we introduce DSS-Prompt, a simple yet effective approach that transforms the pre-trained Vision Transformer with minimal modifications in the way of prompts into a strong FSCIL classifier. Concretely, we synergistically utilize two complementary types of prompts in each Transformer block: static prompts to bridge the domain gap between the pre-training and downstream datasets, thus enabling better adaption; and dynamic prompts to capture instance-aware semantics, thus enabling easy transfer from base to novel classes. Specially, to generate dynamic prompts, we leverage a pre-trained multi-modal model to extract input-related diverse semantics, thereby generating complementary input-aware prompts, and then adaptively adjust their importance across different layers. In this way, on top of the prompted visual embeddings, a simple prototype classifier can beat state-of-the-arts without further training on the incremental tasks. We conduct extensive experiments on four benchmarks to validate the effectiveness of our DSS-Prompt and show that it consistently achieves better performance than existing approaches on all datasets and can alleviate the catastrophic forgetting issue as well.
Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these T2I models often produce images that exhibit semantic misalignments with their corresponding input captions (e.g., missing objects, incorrect attributes), resulting in noisy synthetic image-caption pairs that can hinder model training. Existing dataset pruning techniques are largely designed for removing noisy text in web-crawled data. However, these methods are ill-suited for the distinct challenges of synthetic data, where captions are typically well-formed, but images may be inaccurate representations. To address this gap, we introduce SynC, a novel framework specifically designed to refine synthetic image-caption datasets for ZIC. Instead of conventional filtering or regeneration, SynC focuses on reassigning captions to the most semantically aligned images already present within the synthetic image pool. Our approach employs a one-to-many mapping strategy by initially retrieving multiple relevant candidate images for each caption. We then apply a cycle-consistency-inspired alignment scorer that selects the best image by verifying its ability to retrieve the original caption via image-to-text retrieval. Extensive evaluations demonstrate that SynC consistently and significantly improves performance across various ZIC models on standard benchmarks (MS-COCO, Flickr30k, NoCaps), achieving state-of-the-art results in several scenarios. SynC offers an effective strategy for curating refined synthetic data to enhance ZIC.
Anomaly segmentation aims to identify Out-of-Distribution (OoD) anomalous objects within images. Existing pixel-wise methods typi- cally assign anomaly scores individually and employ a global thresh- olding strategy to segment anomalies. Despite their effectiveness, these approaches encounter significant challenges in real-world applications: (1) neglecting spatial correlations among pixels within the same object, resulting in fragmented segmentation; (2) variabil- ity in anomaly score distributions across image regions, causing global thresholds to either generate false positives in background areas or miss segments of anomalous objects. In this work, we intro- duce OoDDINO, a novel multi-level anomaly segmentation frame- work designed to address these limitations through a coarse-to-fine anomaly detection strategy. OoDDINO combines an uncertainty- guided anomaly detection model with a pixel-level segmentation model within a two-stage cascade architecture. Initially, we propose an Orthogonal Uncertainty-Aware Fusion Strategy (OUAFS) that sequentially integrates multiple uncertainty metrics with visual representations, employing orthogonal constraints to strengthen the detection model's capacity for localizing anomalous regions accurately. Subsequently, we develop an Adaptive Dual-Threshold Network (ADT-Net), which dynamically generates region-specific thresholds based on object-level detection outputs and pixel-wise anomaly scores. This approach allows for distinct thresholding strategies within foreground and background areas, achieving fine- grained anomaly segmentation. The proposed framework is compatible with other pixel-wise anomaly detection models, which act as a plug-in to boost the performance. Extensive experiments on two benchmark datasets validate our framework's superiority and compatibility over state-of-the-art methods. Source code is available at: https://github.com/OoDDINO/OoD-DINO.
Incomplete Multi-view Clustering (IMvC) aims to perform effective clustering in the presence of missing views by exploiting the available information. While many existing approaches demonstrate satisfactory performance, their failure to adequately optimize the recovered data often limits the quality of learned representations and thus hampers clustering performance. To address this challenge, we propose a novel method, Dual-Level Distribution Alignment for Deep Incomplete Multi-View Clustering (DDAIMVC). To effectively address missing data, DDAIMVC employs a fusion-fill strategy to recover incomplete views. The recovered data from each view are then concatenated and processed through an attention mechanism to generate a unified high-level representation. To ensure consistent information across views, the framework performs distribution alignment at both the instance and cluster levels. Specifically, instance-level distribution alignment is conducted by minimizing the maximum mean discrepancy among views, while cluster-level distribution alignment is enhanced via prototypical contrastive learning, which encourages coherent cluster assignments across different modalities. Through the co-optimization of dual-level distribution alignment, the common representation reveals a clear clustering structure. Experimental results on benchmark multi-view datasets demonstrate that DDAIMVC consistently achieves state-of-the-art clustering performance.
Automated choreography generation, which aims to seamlessly harmonize human movements with music, is a multifaceted challenge demanding both technical precision and artistic expressiveness. We present M2PE-DIFF, a novel framework for generating human dance videos conditioned on a reference image and music sequence using a latent diffusion model. Our approach integrates a Music-to-Pose Encoder (M2PEnc), trained with a novel synthetic dataset generation pipeline (SDGPip), which maps audio features into structured 3D pose and shape parameters that capture human geometry and dynamic motion patterns synchronized with musical input. By combining these encoded parameters with a reference image through a multi-level attention mechanism within the latent diffusion framework, we synthesize visually coherent and rhythmically synchronized dance animations of individuals depicted in the given reference image. Experiments on benchmark datasets demonstrate that M2PE-DIFF achieves state-of-the-art performance, producing high-quality dance videos that accurately reflect pose diversity and temporal consistency. Additionally, our method exhibits robust generalization capabilities, validated by its strong performance on a newly introduced in-the-wild dataset.
DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
PDF ↗Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait recognition. Specifically, apart from the 2D silhouette representation of the human body, the proposed pipeline explicitly estimates depth maps from a given RGB image sequence and uses them as a new modality to capture discriminative features inherent in human locomotion. In addition, a novel multi-scale and cross-level fusion scheme has also been developed to bridge the modality gap between depth maps and silhouettes. Extensive experiments on standard benchmarks demonstrate that the proposed DepthGait achieves state-of-the-art performance compared to peer methods and attains an impressive mean rank-1 accuracy on the challenging datasets.
The rapid proliferation of multi-view data has necessitated robust and scalable clustering techniques capable of capturing complex, high-dimensional patterns. While Multi-view Bipartite Graph Clustering (MVBGC) has shown promising results, existing approaches often overlook that the generated bipartite graph is susceptible to disturbances from complex structures and noise. To address these challenges, we propose RTGD-MVC, a novel framework for Robust Tensor Learning with Graph Diffusion tailored for efficient and scalable multi-view graph clustering. RTGD-MVC integrates a graph diffusion mechanism to suppress noise propagation and employs cross-view diffusion to enhance global consistency while capturing complementary information across views. Additionally, a non-convex Tensor Exponential Norm (TEN) is introduced as a tighter surrogate for the tensor rank, enabling the learning of more discriminative and noise-robust representations. By embedding these components into a unified optimization model with linear computational complexity, RTGD-MVC achieves both theoretical efficiency and practical scalability. Extensive experiments on diverse benchmark datasets demonstrate that RTGD-MVC significantly outperforms state-of-the-art methods, highlighting its superior ability to capture intricate multi-view correlations and structural patterns.
Diffusion models have recently shown strong capabilities in image generation. This paper investigates their potential for semantic segmentation, with a focus on RGB-D tasks that demand precise pixel-level predictions. In particular, we delve into the intermediate activations generated during the reverse Markov step of diffusion process, discovering that these activations can effectively capture the semantic information of an input image, making them outstanding representations for addressing segmentation challenges. This paper proposes Diffusion-Enhanced Multi-Modal Segmenter (DiffuSeg), which innovatively combines RGB features with those generated by an additional diffusion model, facilitating the extraction of comprehensive and nuanced semantic features. Furthermore, we propose the Cross Attention-and-Aggregation Module (CAAM), which not only fosters long-range interactions between RGB and diffusion-derived features but also recalibrates both feature sets before integration, enhancing multi-modal synergy. Additionally, our model incorporates a Dynamic Cascade Kernel (DCK) architecture that exploits local and intricate multi-scale geometric details. As a part of DCK, the Spatial Interaction Module (SIM) dynamically encodes spatial information by establishing pixel-level correlations, thereby enhancing the spatial feature representation capacity. Extensive experiments on two benchmark datasets demonstrate the strong capability of DiffuSeg in handling challenging semantic segmentation tasks.
Multi-view classification aims to leverage information from multiple views of data to improve prediction performance by learning complementary and consistent representations. Therefore, in recent years, multi-view learning has attracted widespread attention in the community. Despite the success of existing multi-view learning methods, there are still some challenges when dealing with large-scale multi-view data. To address this issue, we propose a novel Multi-view Hashing Classification (MHC) framework to encode large-scale multi-view data as binary codes, thereby enhancing the semantic discrimination. Specifically, we leverage class prompts to generate corresponding textual descriptions for each instance and learn the corresponding anchor hash codes. To achieve intra-class compactness and inter-class separability, we propose Class-prompt Contrastive Learning (CCL) to enforce class-wise aggregation and separation in the Hamming space. To mitigate the cross-view heterogeneity gap, we propose a Supervised Cross-view Contrastive (SCC) module to align view-specific hash codes under label supervision. Finally, we present Boundary-aware Independent Hashing (BIH) that introduces boundary-aware constraints to reduce class boundary ambiguity, thereby improving the discrimination of fusion hash codes. Nevertheless, we observe that anchor hash codes could violate the bit independence assumption, which potentially hinders the optimization direction. To this end, we adopt a Bit-level Calibration Mechanism (BCM) to filter out redundant bits, thereby restoring bit independence. Extensive experiments conducted on ten benchmark datasets demonstrate the superiority of the proposed MHC in terms of both classification accuracy and inference efficiency. The code is released at https://github.com/Yuhang-lan04/MHC.