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Yuwen Pan, Rui Sun, Wangkai Li, Tianzhu Zhang

Semantic segmentation under adverse conditions is crucial for ensuring robust and accurate visual perception in challenging weather conditions. The distinct characteristics of extreme scenarios hinder traditional segmentation paradigms, highlighting the necessity for tailored approaches for adverse weathers. Due to the scarcity of labeled data in such scenarios, the unsupervised domain adaptation paradigm is commonly utilized to leverage knowledge from normal weather conditions. Although existing methods strive to absorb information from labeled normal weather data and unlabeled adverse condition images, they face significant challenges due to weather unawareness and severe feature heterogeneity, thus struggling to effectively parse scenes under adverse conditions. In this paper, we propose a novel weather-aware aggregation and adaptation network that leverages characteristic knowledge to achieve weather homogenization and enhance scene perception. Specifically, we introduce amplitude prompt aggregation to capture essential characteristics from the Fourier frequency domain that are indicative of different weather conditions. Additionally, we employ weather heterogeneity adaptation to mitigate the inter-domain heterogeneity, thereby achieving feature homogenization across diverse environments. Extensive experimental results on multiple challenging benchmarks demonstrate that our method achieves consistent improvements for semantic segmentation under adverse conditions.

Langyu Wang, Bingke Zhu, Yingying Chen, Yiyuan Zhang, Ming Tang, Jinqiao Wang

The weakly-supervised audio-visual video parsing (AVVP) aims to predict all modality-specific events and locate their temporal boundaries. Despite significant progress, due to the limitations of the weakly-supervised and the deficiencies of the model architecture, existing methods are lacking in simultaneously improving both the segment-level prediction and the event-level prediction. In this work, we propose an audio-visual Mamba network with pseudo labeling aUGmentation (MUG) for emphasising the uniqueness of each segment and excluding the noise interference from the alternate modalities. Specifically, we annotate some of the pseudo-labels based on previous work. Using unimodal pseudo-labels, we perform cross-modal random combinations to generate new data, which can enhance the model's ability to parse various segment-level event combinations. For feature processing and interaction, we employ a audio-visual mamba network. The AV-Mamba enhances the ability to perceive different segments and excludes additional modal noise while sharing similar modal information. Our extensive experiments demonstrate that MUG improves state-of-the-art results on LLP dataset in all metrics ( e.g., gains of 2.1% and 1.2% in terms of visual Segment-level and audio Segment-level metrics). Our code is available at https://github.com/WangLY136/MUG.

Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park, Daniel H. Pak, Anne-Marie Rickmann, Lawrence H. Staib, James S. Duncan, Alex Wong

We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is challenging, as distribution shifts arise from inconsistent imaging protocols and patient variations. Unlike domain adaptation methods that require multiple passes through target data--impractical in clinical settings--our approach adapts pretrained models progressively as they process test data. Our method leverages a shape energy model trained on source data, which assigns an energy score at the patch level to segmentation maps: low energy represents in-distribution (accurate) shapes, while high energy signals out-of-distribution (erroneous) predictions. By minimizing this energy score at test time, we refine the segmentation model to align with the target distribution. To validate effectiveness and adaptability, we evaluated our framework on eight public MRI (bSSFP, T1- and T2-weighted) and X-ray datasets spanning cardiac, spinal cord, and lung segmentation. We consistently outperform baselines both quantitatively and qualitatively. Project page is available at: \href https://voldemort108x.github.io/pttea_seg/ https://voldemort108x.github.io/pttea_seg/

Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, Anpei Chen

Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct supervision on large-scale 3D datasets.In contrast, the limited scale and diversity of available 4D datasets present a major bottleneck for training a highly generalizable 4D model. This constraint has driven conventional 4D methods to fine-tune 3D models on scalable dynamic video data with additional geometric priors such as optical flow and depths. In this work, we take an opposite path and introduce Easi3R, a simple yet efficient training-free method for 4D reconstruction. Our approach applies attention adaptation during inference, eliminating the need for from-scratch pre-training or network fine-tuning. We find that the attention layers in DUSt3R inherently encode rich information about camera and object motion. By carefully disentangling these attention maps, we achieve accurate dynamic region segmentation, camera pose estimation, and 4D dense point map reconstruction. Extensive experiments on real-world dynamic videos demonstrate that our lightweight attention adaptation significantly outperforms previous state-of-the-art methods that are trained or fine-tuned on extensive dynamic datasets.

Yin Xie, Kaicheng Yang, Xiang An, Kun Wu, Yongle Zhao, Weimo Deng, Zimin Ran, Yumeng Wang, Ziyong Feng, Roy Miles 等

Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved impressive zero-shot performance via large-scale vision-language alignment, their reliance on global representations constrains their effectiveness for dense prediction tasks, such as grounding, OCR, and segmentation. To address this gap, we introduce Region-Aware Cluster Discrimination (RICE), a novel method that enhances region-level visual and OCR capabilities. We first construct a billion-scale candidate region dataset and propose a Region Transformer layer to extract rich regional semantics. We further design a unified region cluster discrimination loss that jointly supports object and OCR learning within a single classification framework, enabling efficient and scalable distributed training on large-scale data. Extensive experiments show that RICE consistently outperforms previous methods on tasks, including segmentation, dense detection, and visual perception for Multimodal Large Language Models (MLLMs). The pre-trained models have been released at https://github.com/deepglint/MVT.

Zhuoyuan Li, Jiahao Lu, Jiacheng Deng, Hanzhi Chang, Lifan Wu, Yanzhe Liang, Tianzhu Zhang

The open vocabulary capability of 3D models is increasingly valued, as traditional methods with models trained with fixed categories fail to recognize unseen objects in complex dynamic 3D scenes. In this paper, we propose a simple yet effective approach, SAS, to integrate the open vocabulary capability of multiple 2D models and migrate it to 3D domain. Specifically, we first propose Model Alignment via Text to map different 2D models into the same embedding space using text as a bridge. Then, we propose Annotation-Free Model Capability Construction to explicitly quantify the 2D model's capability of recognizing different categories using diffusion models. Following this, point cloud features from different 2D models are fused with the guide of constructed model capabilities. Finally, the integrated 2D open vocabulary capability is transferred to 3D domain through feature distillation. SAS outperforms previous methods by a large margin across multiple datasets, including ScanNet v2, Matterport3D, and nuScenes, while its generalizability is further validated on downstream tasks, e.g., gaussian segmentation and instance segmentation.

Yinda Chen, Haoyuan Shi, Xiaoyu Liu, Te Shi, Ruobing Zhang, Dong Liu, Zhiwei Xiong, Feng Wu

Neuron segmentation from electron microscopy (EM) volumes is crucial for understanding brain circuits, yet the complex neuronal structures in high-resolution EM images present significant challenges. EM data exhibits unique characteristics including high noise levels, anisotropic voxel dimensions, and ultra-long spatial dependencies that make traditional vision models inadequate. Inspired by autoregressive pretraining in language models, we propose TokenUnify, a hierarchical predictive coding framework that captures multi-scale dependencies through three complementary learning objectives. TokenUnify integrates random token prediction, next-token prediction, and next-all token prediction to create a comprehensive representational space with emergent properties. From an information-theoretic perspective, these three tasks are complementary and provide optimal coverage of visual data structure, with our approach reducing autoregressive error accumulation from O(K) to O(\sqrt K ) for sequences of length K. We also introduce a large-scale EM dataset with 1.2 billion annotated voxels, offering ideal long-sequence visual data with spatial continuity. Leveraging the Mamba architecture's linear-time sequence modeling capabilities, TokenUnify achieves a 44% performance improvement on downstream neuron segmentation and outperforms MAE by 25%. Our approach demonstrates superior scaling properties as model size increases, effectively bridging the gap between pretraining strategies for language and vision models. Code is available at https://github.com/ydchen0806/TokenUnify.

Qizhen Lan, Qing Tian

Dense visual prediction tasks, such as detection and segmentation, are crucial for time-critical applications (e.g., autonomous driving and video surveillance). While deep models achieve strong performance, their efficiency remains a challenge. Knowledge distillation (KD) is an effective model compression technique, but existing feature-based KD methods rely on static, teacher-driven feature selection, failing to adapt to the student's evolving learning state or leverage dynamic student-teacher interactions. To address these limitations, we propose Adaptive student-teacher Cooperative Attention Masking for Knowledge Distillation (ACAM-KD), which introduces two key components: (1) Student-Teacher Cross-Attention Feature Fusion (STCA-FF), which adaptively integrates features from both models for a more interactive distillation process, and (2) Adaptive Spatial-Channel Masking (ASCM), which dynamically generates importance masks to enhance both spatial and channel-wise feature selection. Unlike conventional KD methods, ACAM-KD adapts to the student's evolving needs throughout the entire distillation process. Extensive experiments on multiple benchmarks validate its effectiveness. For instance, on COCO2017, ACAM-KD improves object detection performance by up to 1.4 mAP over the state-of-the-art when distilling a ResNet-50 student from a ResNet-101 teacher. For semantic segmentation on Cityscapes, it boosts mIoU by 3.09 over the baseline with DeepLabV3-MobileNetV2 as the student model.

Tianming Liang, Kun-Yu Lin, Chaolei Tan, Jianguo Zhang, Wei-Shi Zheng, Jian-Fang Hu

Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This is challenging as it involves deep vision-language understanding, pixel-level dense prediction and spatiotemporal reasoning. Despite notable progress in recent years, existing methods still exhibit a noticeable gap when considering all these aspects. In this work, we propose ReferDINO, a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception and cross-modal spatiotemporal reasoning. In detail, ReferDINO integrates two key components: 1) a grounding-guided deformable mask decoder that utilizes location prediction to progressively guide mask prediction through differentiable deformation mechanisms; 2) an object-consistent temporal enhancer that injects pretrained time-varying text features into inter-frame interaction to capture object-aware dynamic changes. Moreover, a confidence-aware query pruning strategy is designed to accelerate object decoding without compromising model performance. Extensive experimental results on five benchmarks demonstrate that our ReferDINO significantly outperforms previous methods (e.g., +3.9% (\mathcal J &\mathcal F ) on Ref-YouTube-VOS) with real-time inference speed (51 FPS).

Ziyang Luo, Nian Liu, Xuguang Yang, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Junwei Han

Audio-Visual Segmentation (AVS) faces a fundamental challenge of effectively aligning audio and visual modalities. While recent approaches leverage foundation models to address data scarcity, they often rely on single-modality knowledge or combine foundation models in an off-the-shelf manner, failing to address the cross-modal alignment challenge. In this paper, we present TAViS, a novel framework that couples the knowledge of multimodal foundation models (ImageBind) for cross-modal alignment and a segmentation foundation model (SAM2) for precise segmentation. However, effectively combining these models poses two key challenges: the difficulty in transferring the knowledge between SAM2 and ImageBind due to their different feature spaces, and the insufficiency of using only segmentation loss for supervision. To address these challenges, we introduce a text-bridged design with two key components: (1) a text-bridged hybrid prompting mechanism where pseudo text provides class prototype information while retaining modality-specific details from both audio and visual inputs, and (2) an alignment supervision strategy that leverages text as a bridge to align shared semantic concepts within audio-visual modalities. Our approach achieves superior performance on single-source, multi-source, semantic datasets, and excels in zero-shot settings.

Jiahao Ma, Tianyu Wang, Miaomiao Liu, David Ahmedt-Aristizabal, Chuong Nguyen

Multiview pedestrian detection typically involves two stages: human modeling and pedestrian localization. Human modeling represents pedestrians in 3D space by fusing multiview information, making its quality crucial for detection accuracy. However, existing methods often introduce noise and have low precision. While some approaches reduce noise by fitting on costly multiview 3D annotations, they often struggle to generalize across diverse scenes. To eliminate reliance on human-labeled annotations and accurately model humans, we propose Depth-Consistency Human Modeling (DCHM), a framework designed for consistent depth estimation and multiview fusion in global coordinates. Specifically, our proposed pipeline iteratively achieves multiview depth consistency in sparse-view, large-scaled, and crowded scenarios, producing precise point clouds for pedestrian localization. Extensive experiments demonstrate that our method significantly reduces noise during human modeling, outperforming previous state-of-the-art baselines. Additionally, to the best of our knowledge, we are the first to reconstruct pedestrians and perform multiview segmentation in such a challenging setting.

Yuanrui Wang, Cong Han, Yafei Li, Zhipeng Jin, Xiawei Li, SiNan Du, Wen Tao, Shuanglong Li, Yi Yang, Chun Yuan 等

Text-to-image generation has transformed content creation, yet precise visual text rendering remains challenging for generative models due to blurred glyphs, semantic inconsistencies, and limited style controllability. Current methods typically employ pre-rendered glyph images as conditional inputs, but their inability to preserve original font styles and color information forces reliance on multi-branch architectures to compensate for missing details. This leads to increased model complexity, higher computational costs, and reduced reusability.To address these limitations, we propose a segmentation-guided framework that leverages pixel-level visual text segmentation masks--complete representations preserving glyph shapes, colors, and spatial details--as unified conditional inputs. Our approach integrates two key innovations: (1) a fine-tuned bilingual segmentation model for extracting precise text masks from source images, and (2) a streamlined diffusion model enhanced with adaptive glyph condition and glyph region loss to ensure semantic and stylistic fidelity. On the AnyText-benchmark, our method achieves a sentence accuracy (Sen.Acc) of 0.8267 and a Normalized Edit Distance (NED) of 0.8976 for Chinese text generation, while the English test set delivers even stronger performance with 0.9018 Sen.Acc and 0.9582 NED, surpassing prior methods by substantial margins. To address broader evaluation needs, we introduce two novel benchmarks: GlyphMM-benchmark (for holistic glyph consistency assessment) and MiniText-benchmark (targeting small-scale glyph fidelity analysis). Experimental results demonstrate our method's dominance across these new benchmarks: 16% Sen.Acc improvement on the Chinese subset of GlyphMM-benchmark and 50% gain on its English counterpart. Notably, our approach achieves over a 100% Sen.Acc boost on the challenging MiniText test set designed for localized text regions. This breakthrough validates our architecture's dual strengths: simplified deployment-ready design and superior generalization for cross-lingual text rendering tasks.

Fang Zhang, Wenzhao Zheng, Linqing Zhao, Zelan Zhu, Jiwen Lu, Xiuzhuang Zhou

3D plane recovery from monocular images constitutes a fundamental task in indoor scene understanding. Recent methods formulate this problem as 2D pixel-level segmentation through convolutional networks or query-based architectures, which purely rely on 2D pixel features while neglecting the inherent 3D spatial nature of planar surfaces. To address this limitation, we propose an end-to-end Plane Reconstruction, Aggregation, and Splatting (PlaneRAS) framework that explicitly leverages 3D geometric reasoning combined with online planar primitive reconstruction. Our framework introduces two core components: 1) a reconstruction module utilizing customized planar primitives to compactly represent 3D scene, and 2) a recovery module that aggregates local primitives to derive globally consistent plane instances. The proposed 3D-aware representation enables direct integration of pretrained geometric priors, significantly enhancing performance beyond conventional 2D-centric approaches. Extensive experiments on ScanNet and NYUv2 datasets demonstrate state-of-the-art results across various evaluation metrics, resulting from our explicit 3D geometric modeling and effective fusion of cross-dimensional features.

Jan Ackermann, Jonas Kulhanek, Shengqu Cai, Haofei Xu, Marc Pollefeys, Gordon Wetzstein, Leonidas J. Guibas, Songyou Peng

In dynamic 3D environments, accurately updating scene representations over time is crucial for applications in robotics, mixed reality, and embodied AI. As scenes evolve, efficient methods to incorporate changes are needed to maintain up-to-date, high-quality reconstructions without the computational overhead of re-optimizing the entire scene.This paper introduces CL-Splats, which incrementally updates Gaussian splatting-based 3D representations from sparse scene captures.CL-Splats integrates a robust change-detection module that segments updated and static components within the scene, enabling focused, local optimization that avoids unnecessary re-computation.Moreover, CL-Splats supports storing and recovering previous scene states, facilitating temporal segmentation and new scene-analysis applications.Our extensive experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art. This establishes a robust foundation for future real-time adaptation in 3D scene reconstruction tasks.We will release our source code and the synthetic and real-world datasets we created to support further research in this area.

Zhuoyan Luo, Yinghao Wu, Tianheng Cheng, Yong Liu, Yicheng Xiao, Hongfa Wang, Xiao-Ping Zhang, Yujiu Yang

The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approaches address GRES by directly extending the well-adopted RES frameworks with object-existence identification. However, these approaches tend to encode multi-granularity object information into a single representation, which makes it difficult to precisely represent comprehensive objects of different granularity. Moreover, the simple binary object-existence identification across all referent scenarios fails to specify their inherent differences, incurring ambiguity in object understanding. To tackle the above issues, we propose a **Co**unting-Aware **H**ierarchical **D**ecoding framework (CoHD) for GRES. By decoupling the intricate referring semantics into different granularity with a visual-linguistic hierarchy, and dynamic aggregating it with intra- and inter-selection, CoHD boosts multi-granularity comprehension with the reciprocal benefit of the hierarchical nature. Furthermore, we incorporate the counting ability by embodying multiple/single/non-target scenarios into count- and category-level supervision, facilitating comprehensive object perception. Experimental results on gRefCOCO, Ref-ZOM, R-RefCOCO, and RefCOCO benchmarks demonstrate the effectiveness and rationality of CoHD which outperforms state-of-the-art GRES methods by a remarkable margin. Code is available.

Shaocong Dong, Lihe Ding, Xiao Chen, Yaokun Li, Yuxin Wang, Yucheng Wang, Qi Wang, Jaehyeok Kim, Chenjian Gao, Zhanpeng Huang 等

To generate 3D objects, early research focused on multi-view-driven approaches relying solely on 2D renderings. Recently, the 3D native latent diffusion paradigm has demonstrated superior performance in 3D generation, because it fully leverages the geometric information provided in ground truth 3D data. Despite its fast development, 3D diffusion still faces three challenges. First, the majority of these methods represent a 3D object by one single latent, regardless of its complexity. This may lead to detail loss when generating 3D objects with multiple complicated parts. Second, most 3D assets are designed parts by parts, yet the current holistic latent representation overlooks the independence of these parts and their interrelationships, limiting the model's generative ability. Third, current methods rely on global conditions (e.g., text, image, point cloud) to control the generation process, lacking detailed controllability. Therefore, motivated by how 3D designers create a 3D object, we present a new part-based 3D generation framework, CoPart, which represents a 3D object with multiple contextual part latents and simultaneously generates coherent 3D parts. This part-based framework has several advantages, including: i) reduces the encoding burden of intricate objects by decomposing them into simpler parts, ii) facilitates part learning and part relationship modeling, and iii) naturally supports part-level control. Furthermore, to ensure the coherence of part latents and to harness the powerful priors from foundation models, we propose a novel mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising. Benefiting from the part-based representation, we demonstrate that CoPart can support various applications including part-editing, articulated object generation, and mini-scene generation. Moreover, we collect a new large-scale 3D part dataset named Partverse from Objaverse through automatic mesh segmentation and subsequent human post-annotations. By training on the proposed dataset, CoPart achieves promising part-based 3D generation with high controllability. Project page: https://hkdsc.github.io/project/copart.

Do Huu Dat, Nam Hyeon-Woo, Po-Yuan Mao, Tae-Hyun Oh

Text-to-image diffusion models have shown impressive capabilities in generating realistic visuals from natural-language prompts, yet they often struggle with accurately binding attributes to corresponding objects, especially in prompts containing multiple attribute-object pairs. This challenge primarily arises from the limitations of commonly used text encoders, such as CLIP, which can fail to encode complex linguistic relationships and modifiers effectively. Existing approaches have attempted to mitigate these issues through attention map control during inference and the use of layout information or fine-tuning during training, yet they face performance drops with increased prompt complexity. In this work, we introduce a novel compositional generation method that leverages pairwise image embeddings to improve attribute-object binding. Our approach decomposes complex prompts into sub-prompts, generates corresponding images, and computes visual prototypes that fuse with text embeddings to enhance representation. By applying segmentation-based localization training, we address cross-attention misalignment, achieving improved accuracy in binding multiple attributes to objects. Our approaches outperform existing compositional text-to-image diffusion models on the benchmark T2I CompBench, achieving better image quality, evaluated by humans, and emerging robustness under scaling the number of binding pairs in the prompt.

Dong Zhao, Qi Zang, Shuang Wang, Nicu Sebe, Zhun Zhong

Pseudo-labeling is a key technique of semi-supervised and cross-domian semantic segmentation, yet its efficacy is often hampered by the intrinsic noise of pseudo-labels. This study introduces Pseudo-SD, a novel framework that redefines the utilization of pseudo-label knowledge through Stable Diffusion (SD). Our Pseudo-SD innovatively combines pseudo-labels and its text prompts to fine-tune SD models, facilitating the generation of high-quality, diverse synthetic images that closely mimic target data characteristics. Within this framework, two novel mechanisms, i.e., partial attention manipulation, and structured pseudo-labeling, are proposed to effectively spread text-to-image corresponding during SD fine-tuning process and to ensure controllable high-quality image synthesis respectively. Extensive results demonstrate that Pseudo-SD significantly improves the performance on semi-supervised and cross-domain segmentation scenarios. By injecting our Pseudo-SD into current methods, we establish new state-of-the-arts in different datasets, offering a new way for the exploration of effective pseudo-label utilization. The source code is available at \href https://github.com/DZhaoXd/Pseudo-SD https://github.com/DZhaoXd/Pseudo-SD .

Lorenzo Mur-Labadia, Maria Santos-Villafranca, Jesus Bermudez-Cameo, Alejandro Perez-Yus, Ruben Martinez-Cantin, Jose J. Guerrero

Understanding the world from multiple perspectives is essential for intelligent systems operating together, where segmenting common objects across different views remains an open problem. We introduce a new approach that re-defines cross-image segmentation by treating it as a mask matching task. Our method consists of: (1) A Mask-Context Encoder that pools dense DINOv2 semantic features to obtain discriminative object-level representations from FastSAM mask candidates, (2) an Ego-Exo Cross-Attention that fuses multi-perspective observations, (3) a Mask Matching contrastive loss that aligns cross-view features in a shared latent space, and (4) a Hard Negative Adjacent Mining strategy to encourage the model to better differentiate between nearby objects. O-MaMa achieves the state of the art in the Ego-Exo4D Correspondences benchmark, obtaining relative gains of +22 % and +76 % in the Ego2Exo and Exo2Ego IoU against the official challenge baselines, and a +13 % and +6 % compared with the SOTA with 1 % of the training parameters.

Qi Chen, Lingxiao Yang, Yun Chen, Nailong Zhao, Jianhuang Lai, Jie Shao, Xiaohua Xie

Fine-tuning pre-trained vision-language models has emerged as a powerful approach for enhancing open-vocabulary semantic segmentation (OVSS). However, the substantial computational and resource demands associated with training on large datasets have prompted interest in training-free methods for OVSS. Existing training-free approaches primarily focus on modifying model architectures and generating prototypes to improve segmentation performance. However, they often neglect the challenges posed by class redundancy, where multiple categories are not present in the current test image, and visual-language ambiguity, where semantic similarities among categories create confusion in class activation. These issues can lead to suboptimal class activation maps and affinity-refined activation maps. Motivated by these observations, we propose FreeCP, a novel training-free class purification framework designed to address these challenges. FreeCP focuses on purifying semantic categories and rectifying errors caused by redundancy and ambiguity. The purified class representations are then leveraged to produce final segmentation predictions. We conduct extensive experiments across eight benchmarks to validate FreeCP's effectiveness. Results demonstrate that FreeCP, as a plug-and-play module, significantly boosts segmentation performance when combined with other OVSS methods.