Rethinking MLLM Itself as a Segmenter with a Single Segmentation Token
Beijing Institute of Technology（北京理工大学）；University of Birmingham（伯明翰大学）；Beijing Jiaotong University（北京交通大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Anqi Zhang, Xiaokang Ji, Guangyu Gao, Jianbo Jiao, Chi Harold Liu, Yunchao Wei
Recent segmentation methods leveraging Multi-modal Large Language Models (MLLMs) have shown reliable object-level segmentation and enhanced spatial perception. However, almost all previous methods predominantly rely on specialist mask decoders to interpret masks from generated segmentation-related embeddings and visual features, or incorporate multiple additional tokens to assist. This paper aims to investigate whether and how we can unlock segmentation from MLLM itSELF with 1 segmentation Embedding (SELF1E) while achieving competitive results, which eliminates the need for external decoders. To this end, our approach targets the fundamental limitation of resolution reduction in pixel-shuffled image features from MLLMs. First, we retain image features at their original uncompressed resolution, and refill them with residual features extracted from MLLM-processed compressed features, thereby improving feature precision. Subsequently, we integrate pixel-unshuffle operations on image features with and without LLM processing, respectively, to unleash the details of compressed features and amplify the residual features under uncompressed resolution, which further enhances the resolution of refilled features. Moreover, we redesign the attention mask with dual perception pathways, i.e., image-to-image and image-to-segmentation, enabling rich feature interaction between pixels and the segmentation token. Comprehensive experiments across multiple segmentation tasks validate that SELF1E achieves performance competitive with specialist mask decoder-based methods, demonstrating the feasibility of decoder-free segmentation in MLLMs. Project page: https://github.com/ANDYZAQ/SELF1E.
NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；Sensetime Research（商汤科技研究院）；Nanjing University（南京大学）
RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning
School of Electrical and Electronic Engineering, Nanyang Technological University（电气电子工程学院，南洋理工大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics（教育部计算与经济交叉学科重点实验室，上海财经大学）
GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；ByteDance（字节跳动）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究所）；Xi’an Jiaotong University（西安交通大学）；Renmin University of China（中国人民大学）
Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond
The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Tencent（腾讯）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；Westlake University（西湖大学）
LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning
School of AI, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Kling Team, Kuaishou Technology（快手科技 Kling 团队）；Peking University（北京大学）；HKUST（香港科技大学）；CASIA（中国科学院自动化研究所）；Nanjing University（南京大学）；Renmin University of China（中国人民大学）；Tsinghua University（清华大学）
本文提出LatentOmni框架，通过统一的音频-视觉潜在空间进行多模态推理，利用特征级监督和Omni-Sync Position Embedding保持时间一致性，从而在多个音频-视觉推理基准测试中取得最佳性能。
IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tencent PCG（腾讯平台与内容事业群）；The Hong Kong University of Science and Technology（香港科技大学）；Dalian University of Technology（大连理工大学）
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件工程国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学医疗数据科学国家研究院）
