Instance-level object segmentation across disparate egocentric and exocentric views is a fundamental challenge in visual understanding, critical for applications in embodied AI and remote collaboration. This task is exceptionally difficult due to severe changes in scale, perspective, and occlusion, which destabilize direct pixel-level matching. While recent geometry-aware models like VGGT provide a strong foundation for feature alignment, we find they often fail at dense prediction tasks due to significant pixel-level projection drift, even when their internal object-level attention remains consistent. To bridge this gap, we introduce VGGT-Segmentor (VGGT-S), a framework that unifies robust geometric modeling with pixel-accurate semantic segmentation. VGGT-S leverages VGGT's powerful cross-view feature representation and introduces a novel Union Segmentation Head. This head operates in three stages: mask prompt fusion, point-guided prediction, and iterative mask refinement, effectively translating high-level feature alignment into a precise segmentation mask. Furthermore, we propose a single-image self-supervised training strategy that eliminates the need for paired annotations and enables strong generalization. On the Ego-Exo4D benchmark, VGGT-S sets a new state-of-the-art, achieving 67.7% and 68.0% average IoU for Ego to Exo and Exo to Ego tasks, respectively, significantly outperforming prior methods. Notably, our correspondence-free pretrained model surpasses most fully-supervised baselines, demonstrating the effectiveness and scalability of our approach. Code is publicly available at: https://github.com/buaa-colalab/VGGT-S.
Making Training-Free Diffusion Segmentors Scale with the Generative Power
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；School of Cyber Science and Tech., Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）；Alibaba Group（阿里巴巴集团）；School of Computer Science and Tech., University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Key Laboratory of Big Data Mining and Knowledge Management, CAS（中国科学院大数据挖掘与知识管理重点实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（脑启发智能科学与技术研究院，复旦大学）；Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University（上海智能信息处理重点实验室，复旦大学计算机学院）
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
The Chinese University of Hong Kong（香港中文大学）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology（广东省计算机视觉与虚拟现实技术重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Instance Brownian Bridge as Texts for Open-vocabulary Video Instance Segmentation
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Tsinghua University（清华大学）；Xiamen University（厦门大学）；Peng Cheng Laboratory（鹏城实验室）
本文提出 BriVIS，通过 TIR 将帧级实例查询连接为布朗桥，并利用 BTA 对比学习实现桥级实例表示与文本对齐，在 BURST 上显著优于 OV2Seg。
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；University of Liverpool（利物浦大学）；Image Processing Center, School of Astronautics, Beihang University（北京航空航天大学宇航学院图像处理中心）
Mask Propagation for Efficient Video Semantic Segmentation
ZIP Lab, Monash University（莫纳什大学）；Baidu Inc.（百度公司）；ReLER, AAII, UTS（悉尼科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；SenseTime（商汤科技）
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；Shanghai AI laboratory（上海人工智能实验室）；West China Hospital, Sichuan University（四川大学华西医院）；West China Biomedical Big Data Center, West China Hospital, Sichuan University（四川大学华西医院华西生物医学大数据中心）；SenseTime Research（商汤科技研究院）
RSAgent: Learning to Reason and Act for Text-Guided Segmentation via Multi-Turn Tool Invocations
Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University（上海智能信息处理关键实验室，计算机科学与人工智能学院，复旦大学）；Artificial Intelligence, Fudan University（人工智能，复旦大学）
PLAS-Net: Pixel-Level Area Segmentation for UAV-Based Beach Litter Monitoring
Graduate School of Frontier Sciences, The University of Tokyo, Japan（前沿科学研究生院，东京大学，日本）；Department of Urban Informatics, Shenzhen University, China（城市信息化系，深圳大学，中国）
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
