Automatic segmentation of echocardiography videos is crucial for computer-aided cardiovascular function assessment in clinical practice. However, it is a challenging task owing to the existence of massive speckle noise, the large shape variations of heart structures between frames, and limited annotations. In this paper, we propose a novel semi-supervised video segmentation model to comprehensively meet these challenges. The proposed approach has two key techniques. First, we propose a dual-stream architecture that processes spatial and temporal features through separate pathways to capture structural details and motion patterns, then enhances spatiotemporal representations by interacting these decomposed features with query features generated from the original input. Second, as speckle noise primarily concentrates in high-frequency regions, we extend the traditional dilated convolution from a frequency perspective, enabling it to adaptively adjust the dilation rate and convolution kernel weights based on high frequency speckle noise information. This enables the network to focus on specific frequency bands, thereby enhancing its ability to capture both low-frequency context and high-frequency local details. Extensive experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of both accuracy and inference speed. Codes are available at https://github.com/guojx2255/HSCA-SDC.
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Referring video object segmentation (RVOS) focuses on segmenting target objects in a video based on natural language descriptions. However, existing methods typically rely on text cues that are unrelated to video content, and the target entity is only recognized in the pixel space. This often leads to ambiguous cross-modal understanding and fragmented perception across space and time, resulting in inaccurate or incomplete segmentation of the target objects. To address these challenges, a novel wavelet calibration learning (WaveCL) framework is proposed to unify cross-modal understanding and preserve spatial-temporal integrity of the target object. The WaveCL framework is built on two core components: semantic-calibrated entity perception (SEP) and wavelet-guided integrity perception (WIP). SEP aligns the textual semantics with video content, enabling more accurate and context-aware cross-modal understanding. WIP, on the other hand, leverages wavelet representations to capture fine-grained details of the target object from a global spatial-temporal perspective. By refining wavelet clues with the guidance of text queries, WIP enhances the integrity of segmentation. Through the collaboration of SEP and WIP, WaveCL enables precise, target-specific segmentation with detailed boundaries and consistent spatial-temporal perception. Extensive experiments on four benchmark datasets of Ref-YouTube-VOS, Ref-DAVIS17, A2D-Sentences, and JHMDB-Sentences show that WaveCL outperforms existing state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.
While promptable segmentation (e.g., SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In contrast, task-generic promptable segmentation aims to reduce the need for such detailed prompts by employing only a task-generic prompt to guide segmentation across all test samples. However, when applied to Camouflaged Object Segmentation (COS), current methods still face two critical issues: 1) semantic ambiguity in getting instance-specific text prompts, which arises from insufficient discriminative cues in holistic captions, leading to foreground-background confusion; 2) semantic discrepancy combined with spatial separation in getting instance-specific visual prompts, which results from global background sampling far from object boundaries with low feature correlation, causing SAM to segment irrelevant regions. To mitigate the issues above, we propose RDVP-MSD, a novel training-free test-time adaptation framework that synergizes Region-constrained Dual-stream Visual Prompting (RDVP) via Multimodal Stepwise Decomposition Chain of Thought (MSD-CoT). MSD-CoT progressively disentangles image captions to eliminate semantic ambiguity, while RDVP injects spatial constraints into visual prompting and independently samples visual prompts for foreground and background points, effectively mitigating semantic discrepancy and spatial separation. Without requiring any training or supervision, RDVP-MSD achieves a state-of-the-art segmentation result on multiple COS benchmarks. The codes will be available at https://github.com/ycyinchao/RDVP-MSD.
Unsupervised Ego- and Exo-centric Dense Procedural Activity Captioning via Gaze Consensus Adaptation
Even from an early age, humans naturally adapt between exocentric (Exo) and egocentric (Ego) perspectives to understand daily procedural activities. Inspired by this cognitive ability, we propose a novel Unsupervised Ego-Exo Dense Procedural Activity Captioning (UE^2 DPAC) task, which aims to transfer knowledge from the labeled source view to predict the time segments and descriptions of action sequences for the target view without annotations. Despite previous works endeavoring to address the fully-supervised single-view or cross-view dense video captioning, they lapse in the proposed task due to the significant inter-view gap caused by temporal misalignment and irrelevant object interference. Hence, we propose a Gaze Consensus-guided Ego-Exo Adaptation Network (GCEAN) that injects the gaze information into the learned representations for the fine-grained Ego-Exo alignment. Specifically, we propose a Score-based Adversarial Learning Module (SALM) that incorporates a discriminative scoring network and compares the scores of distinct views to learn unified view-invariant representations from a global level. Then, the Gaze Consensus Construction Module (GCCM) utilizes the gaze to progressively calibrate the learned representations to highlight the regions of interest and extract the corresponding temporal contexts. Moreover, we adopt hierarchical gaze-guided consistency losses to construct gaze consensus for the explicit temporal and spatial adaptation between the source and target views. To support our research, we propose a new EgoMe-UE^2 DPAC benchmark, and extensive experiments demonstrate the effectiveness of our method, which outperforms many related methods by a large margin. Code is available at https://github.com/ZhaofengSHI/GCEAN.
Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled fast and high-quality scene reconstruction, research has also explored incorporating open-vocabulary understanding into 3DGS. However, most existing methods require iterative optimization over per-view 2D semantic feature maps, which not only results in inefficiencies but also leads to inconsistent 3D semantics across views. To address these limitations, we introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives. The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities and providing a structured foundation for open-vocabulary understanding. Based on the graph structure, we design an efficient reprojection strategy that lifts 2D semantic features onto the superpoints, avoiding costly multi-view iterative training. The resulting representation ensures strong 3D semantic coherence and naturally supports hierarchical understanding, enabling both coarse- and fine-grained open-vocabulary perception within a unified semantic field. Extensive experiments demonstrate that our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over 30× faster.
In recent years, Transformers have achieved remarkable progress in computer vision tasks. However, their global modeling often comes with substantial computational overhead, in stark contrast to the human eye's efficient information processing. Inspired by the human eye's sparse scanning mechanism, we propose a Sparse Scan Self-Attention mechanism (S3A). This mechanism predefines a series of Anchors of Interest for each token and employs local attention to efficiently model the spatial information around these anchors, avoiding redundant global modeling and excessive focus on local information. This approach mirrors the human eye's functionality and significantly reduces the computational load of vision models. Building on S3A, we introduce the Sparse Scan Vision Transformer (SSViT). Extensive experiments demonstrate the outstanding performance of SSViT across a variety of tasks. Specifically, on ImageNet classification, without additional supervision or training data, SSViT achieves top-1 accuracies of 84.4%/85.7% with 4.4G/18.2G FLOPs. SSViT also excels in downstream tasks such as object detection, instance segmentation, and semantic segmentation. Its robustness is further validated across diverse datasets.
Cross-category object perception is one of the essential upstream tasks for generelizable robot object interaction and manipulation. Recently, an increasing number of researchers are focusing on investigating visual Generalizable and Actionable Parts understanding at cross-category level perception. However, these works are built upon the RGB-D or point cloud input, that relies on the depth information capture. Under the circumstances of limited depth camera performance, e.g. transparent or light absorbing material, perception algorithms that do not require depth information are urgently needed. In this paper, we propose DFGAP, a novel depth-free framework for RGB-based GAParts segmentation and pose estimation. Specifically, we independently model the ill-pose problems from the absence of depth for GAPart segmentation and pose estimation, by clearly quantifying the pixel-wise segmentation probability and relative depth. We reduce the uncertainty and benefit learning in these two tasks. The experimental results demonstrate the superior performance and robustness of our DFGAP. Our work provides a new research paradigm in GAParts perception. We believe that our work has the enormous potential to be applied in many areas of embodied AI system.
Visible watermark removal is crucial for evaluating watermark robustness and advancing more resilient protection techniques. Current methods face challenges in real-world scenarios due to architectural constraints in multi-task frameworks and limited dataset diversity. To address these challenges, we first propose a novel two-stage framework, PatchWiper, consisting of an independent watermark segmentation network and a highly dynamic patch-wise restoration network. This framework decouples watermark localization from background restoration, allowing each network to focus on its designated task. Our restoration network dynamically generates unique parameters for each image patch, enabling fine-grained adaptation to different watermark distortions. Second, we construct the Pixabay Real-world Watermark Dataset (PRWD ), which incorporates diverse background images and over 1,000 distinct watermark types, providing a more comprehensive benchmark for evaluating watermark removal methods. Extensive experiments on PRWD, ILAW, and real-world testing images demonstrate our method's superior performance over existing approaches, particularly in handling complex real-world cases.
In histopathology, tissue sections are typically stained using common H&E staining or special stains (MAS, PAS, PASM, etc. ) to clearly visualize specific tissue structures. The rapid advancement of deep learning offers an effective solution for generating virtually stained images, significantly reducing the time and labor costs associated with traditional histochemical staining. However, a new challenge arises in separating the fundamental visual characteristics of tissue sections from the visual differences induced by staining agents. Additionally, virtual staining often overlooks essential pathological knowledge and the physical properties of staining, resulting in only style-level transfer. To address these issues, we introduce, for the first time in virtual staining tasks, a pathological vision-language large model (VLM) as an auxiliary tool. We integrate contrastive learnable prompts, foundational concept anchors for tissue sections, and staining-specific concept anchors to leverage the extensive knowledge of the pathological VLM. This approach is designed to describe, frame, and enhance the direction of virtual staining. Furthermore, we have developed a data augmentation method based on the constraints of the VLM. This method utilizes the VLM's powerful image interpretation capabilities to further integrate image style and structural information, proving beneficial in high-precision pathological diagnostics. Extensive evaluations on publicly available multi-domain unpaired staining datasets demonstrate that our method can generate highly realistic images and enhance the accuracy of downstream tasks, such as glomerular detection and segmentation. Our code. https://github.com/CZZZZZZZZZZZZZZZZZ/VPGAN-HARBOR is available.
Semi-supervised Video Object Segmentation (VOS) aims to segment a user-specified object across all frames of a video using only the first frame's annotated mask. A key challenge in VOS is simultaneously preserving object identity and maintaining precise segmentation in dynamic scenes, especially during rapid motion. Many existing methods use previous frame masks as positional constraints, hindering segmentation of newly exposed regions-an issue known as over-suppression. To address these challenges, we propose FlowTrack, integrating two main components: an Adjacent-frame Motion Tracker (AMT) and an Adaptive Motion Predictor (AMP). AMT explicitly captures motion and positional information between adjacent frames and fuses it with historical target cues, improving segmentation constraint robustness and helping maintain stable object identity during rapid motion or significant deformation. However, relying primarily on historical masks and frame-level constraints may fail to accurately predict sudden changes in motion states, which still leads to over-suppression. To overcome this limitation, AMP predicts future states from historical motion data. Employing a learned state predictor and a Kalman-inspired recursive measurement fusion, AMP adapts to complex and abrupt motion changes. This dynamic prediction-update scheme refines segmentation boundaries, compensates for historical constraints, and effectively mitigates over-suppression. Experimental results on standard VOS benchmarks validate the effectiveness of the proposed FlowTrack framework in handling challenging dynamic scenes involving rapid motion and addressing the over-suppression issue.
In existing Sign Language (SL) research, most datasets and backbone models focus on sentence-level samples. However, the annotated sentence-level SL datasets are rather limited, and it is in great need to expand sentence-level SL datasets. When considering the large-scale long SL videos with captions, we propose a new task, i.e., Sentence-level Sign Language Segmentation (SSLS), which splits the long videos into consecutive sentence-level videos. SSLS is an important and meaningful task, which can greatly reduce the labor costs in data annotation for sentence-level SL datasets. However, SSLS is a very challenging task, since it is rather difficult to accurately find the boundary of each sentence in a long video. To address this issue, we formalize, learn, and optimize the boundaries of sentences step by step. First, to distinguish the boundary and the inside of a sentence, we formalize SSLS as a frame-level classification task and design a boundary annotation scheme. Second, to learn the boundary of each sentence from the long video, we design a multimodal framework, SignBD, which correlates the local features and global features through dual dilated attention, while aligning visual and textual (i.e., sentences) modalities through gated cross-attention. Third, to alleviate the widely existed over-segmentation and under-segmentation problems in segmentation tasks, we propose a boundary optimization strategy, which utilizes the number of sentences provided by captions to optimize (i.e., insert or delete) boundaries based on information uncertainty. Extensive experimental results demonstrate the superiority of our solution.
In clinical practice, medical image analysis often requires efficient execution on resource-constrained mobile devices. However, existing mobile models-primarily optimized for natural images-tend to perform poorly on medical tasks due to the significant information density gap between natural and medical domains. Combining computational efficiency with medical imaging-specific architectural advantages remains a challenge when developing lightweight, universal, and high-performing networks. To address this, we propose a mobile model called Mobile U-shaped Vision Transformer (Mobile U-ViT) tailored for medical image segmentation. Specifically, we employ the newly proposed ConvUtr as a hierarchical patch embedding, featuring a parameter-efficient large-kernel CNN with inverted bottleneck fusion. This design exhibits transformer-like representation learning capacity while being lighter and faster. To enable efficient local-global information exchange, we introduce a novel Large-kernel Local-Global-Local (LKLGL) block that effectively balances the low information density and high-level semantic discrepancy of medical images. Finally, we incorporate a shallow and lightweight transformer bottleneck for long-range modeling and employ a cascaded decoder with downsampled skip connections for dense prediction. Despite its reduced computational demands, our medical-optimized architecture achieves state-of-the-art performance across eight public 2D and 3D datasets covering diverse imaging modalities, including zero-shot testing on four unseen datasets. These results establish it as an efficient yet powerful and generalization solution for mobile medical image analysis. Code is available at: https://github.com/FengheTan9/Mobile-U-ViT.
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.
Referring video object segmentation (RVOS) extracts objects from videos based on provided text narrations. Previous approaches typically work on all video frames simultaneously through offline processing, but this is not always possible. Offline processing becomes also ineffective for long videos, as directly cutting the video into short clips to fit memory limits results in the loss of temporal consistency. To make RVOS more applicable to real-world video streams or long video scenarios, we introduce a Filtering Framework for RVOS (FF-RVOS), the first model capable of operating in online, semi-online, and offline modes with just one training session. We redesign RVOS as a stochastic optimization problem and leverage filtering to optimize object states across temporal sequences. Our method enhances temporal consistency for video streams and when a long video is processed in shorter clip sequences due to memory limitations. FF-RVOS demonstrates superior performance compared to previous state-of-the-art methods on public benchmarks with clear improvements, especially when the video is cut into short clips for processing. Our framework can also be embedded into different offline methods to boost temporal consistency. The project page is https://github.com/haliphinx/FF-RVOS.
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.
Existing medical vision-language contrastive pretraining methods aim to bring the paired image-report embeddings close together while pushing the unpaired ones apart. However, medical images often exhibit high inter-class visual similarity with only subtle differences, leading to the presence of hard negative samples that are semantically distinct from the anchor but incorrectly close to it in the embedding space, making it challenging to distinguish semantically dissimilar samples. Previous methods consider only the embedding similarity between samples to identify hard negatives, often wrongly treating false negatives as hard negatives. To address this issue, we design a simple yet effective approach called Semantic-Aware Hard Negative mining (SAHN), distinguishing hard negatives from false negatives and encouraging the model to pay greater attention to hard negatives. Specifically, hard negatives are identified as samples with high embedding similarity but low semantic similarity to the anchor and assigned greater importance weights. By integrating these importance weights into the InfoNCE loss, SAHN enhances the model's ability to separate semantically dissimilar samples while clustering semantically similar ones. We further conduct a gradient-based theoretical analysis to validate the effectiveness of SAHN. Extensive experimental results on four downstream medical tasks covering image classification, object detection, semantic segmentation, and cross-modal retrieval demonstrate the superiority of our approach.
In few-shot medical image segmentation, most existing methods focus heavily on learning explicit correlations between support and query sets, often overlooking the core demands of the segmentation task itself. In this work, we identify three overlooked yet critical issues that limit current performance: the diversity of background distributions, the degradation of support prototypes, and the over-activation of irrelevant regions. To address these challenges, we propose a novel framework with three lightweight and adaptive modules. First, a background self-distillation module acts as a self-attention-driven agent to cluster and aggregate diverse background features, generating multiple sub-prototypes that enhance foreground-background separation. Second, we introduce a prototype self-anchoring mechanism that leverages a dual-branch correlation mapping and reverse supervision to stabilize support prototype learning and prevent feature degradation. Third, an activation self-calibration module identifies over-activated residuals and applies test-time channel manipulation to suppress noisy activations without additional training. Extensive experiments on standard few-shot medical segmentation benchmarks demonstrate the superiority of our approach over state-of-the-art methods. Our findings suggest that performance gains come not only from better support-query alignment, but also from rethinking and addressing the often neglected aspects of few-shot segmentation.
3D Visual Grounding (3DVG) aims to localize objects in 3D scenes using natural language descriptions. Although supervised methods achieve higher accuracy in constrained settings, zero-shot 3DVG holds greater promise for real-world applications since eliminating scene-specific training requirements. However, existing zero-shot methods face challenges of spatial-limited reasoning due to reliance on single-view localization, and contextual omissions or detail degradation. To address these issues, we propose SeqVLM, a novel zero-shot 3DVG framework that leverages multi-view real-world scene images with spatial information for target object reasoning. Specifically, SeqVLM first generates 3D instance proposals via a 3D semantic segmentation network and refines them through semantic filtering, retaining only semantic-relevant candidates. A proposal-guided multi-view projection strategy then projects these candidate proposals onto real scene image sequences, preserving spatial relationships and contextual details in the conversion process of 3D point cloud to images. Furthermore, to mitigate VLM computational overload, we implement a dynamic scheduling mechanism that iteratively processes sequances-query prompts, leveraging VLM's cross-modal reasoning capabilities to identify textually specified objects. Experiments on the ScanRefer and Nr3D benchmarks demonstrate state-of-the-art performance, achieving Acc@0.25 scores of 55.6% and 53.2%, surpassing previous zero-shot methods by 4.0% and 5.2%, respectively, which advance 3DVG toward greater generalization and real-world applicability.
Despite the effectiveness of Segment Anything Model (SAM) based methods in Few-Shot Segmentation (FSS) tasks, our closer examination of their prompt encoding mechanism reveals that these methods rely solely on visual information to generate a single type of prompt. Consequently, they suffer from semantic granularity representation bias and a loss of spatial information. To address these limitations, this paper introduces an innovative multi-modal prompt encoder, enabling SAM to leverage both annotated reference images and textual descriptions of class names as segmentation prompts. This approach generates text prompts, dense visual prompts, and sparse visual prompts, spanning multiple modalities and granularities. These prompts provide enhanced representations of the target class, capturing both abstract semantics and specific details, while ensuring granularity appropriateness. When our multi-modal prompt encoder is integrated with SAM's image encoder and mask decoder, the overall model is referred to as MM-Prompt. To validate its effectiveness, we conducted extensive empirical studies on the PASCAL-5^i and COCO-20^i datasets. The experimental results demonstrate that MM-Prompt achieves state-of-the-art performance in FSS tasks, highlighting its substantial potential and value in this domain.
3D semantic field learning is crucial for applications like autonomous navigation, AR/VR, and robotics, where accurate comprehension of 3D scenes from limited viewpoints is essential. Existing methods struggle under sparse view conditions, relying on inefficient per-scene multi-view optimizations, which are impractical for many real-world tasks. To address this, we propose SLGaussian, a feed-forward method for constructing 3D semantic fields from sparse viewpoints, allowing direct inference of 3DGS-based scenes. By ensuring consistent SAM segmentations through video tracking and using low-dimensional indexing for high-dimensional CLIP features, SLGaussian efficiently embeds language information in 3D space, offering a robust solution for accurate 3D scene understanding under sparse view conditions. In experiments on two-view sparse 3D object querying and segmentation in the LERF and 3D-OVS datasets, SLGaussian outperforms existing methods in chosen IoU, Localization Accuracy, and mIoU. Moreover, our model achieves scene inference in under 30 seconds and open-vocabulary querying in just 0.011 seconds per query.