Multimodal Large Language Models (MLLMs) have recently achieved remarkable success in visual-language understanding, demonstrating superior high-level semantic alignment within their vision encoders. An important question thus arises: Can these encoders serve as versatile vision backbones, capable of reliably performing classic vision-centric tasks as well? To address the question, we make the following contributions: (i) we identify that the vision encoders within MLLMs exhibit deficiencies in their dense feature representations, as evidenced by their suboptimal performance on dense prediction tasks (e.g., semantic segmentation, depth estimation); (ii) we propose VersaViT, a well-rounded vision transformer that instantiates a novel multi-task framework for collaborative post-training. This framework facilitates the optimization of the vision backbone via lightweight task heads with multi-granularity supervision; (iii) extensive experiments across various downstream tasks demonstrate the effectiveness of our method, yielding a versatile vision backbone suited for both language-mediated reasoning and pixel-level understanding.
Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所分子影像重点实验室）；School of Software, North University of China（中北大学软件学院）
Unleashing MLLMs on the Edge: A Unified Framework for Cross-Modal ReID via Adaptive SVD Distillation
Tencent Youtu Lab, Shanghai, China（腾讯优图实验室，上海，中国）；Xiamen University, Media Analytics（厦门大学，媒体分析）；Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen, China（计算实验室，人工智能系，信息学院，厦门，中国）
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs
Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Georgia Institute of Technology（佐治亚理工学院）；Nanjing University of Science and Technology（南京理工大学）；University of Oxford（牛津大学）；The Ohio State University（俄亥俄州立大学）
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Keio University（庆应义塾大学）；National Institute of Informatics（国立信息学研究所）；National Institute of Informatics Research and Development Center for Large Language Models（国立信息学研究所大型语言模型研发中心）
Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Vermont Artificial Intelligence Lab, Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系佛蒙特人工智能实验室）；Intelligent Machines Lab, Department of Artificial Intelligence, Information Technology University（信息技术大学人工智能系智能机器实验室）；Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）
Visual Prompting in Multimodal Large Language Models: A Survey
UC San Diego（加州大学圣地亚哥分校）；Dartmouth College（达特茅斯学院）；Rutgers University（罗格斯大学）；UC Los Angeles（加州大学洛杉矶分校）；Adobe Research（奥多比研究院）；The University of New South Wales（新南威尔士大学）；CSIRO’s Data61（联邦科学与工业研究组织Data61）
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tencent（腾讯）；Ningbo Global Innovation Center, Zhejiang University（宁波全球创新中心，浙江大学）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字智能服务技术重点实验室）
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（东华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Institute of High Performance Computing, A*STAR（高性能计算研究所，A*STAR）
Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning
College of AI, Tsinghua University, Beijing, China（清华大学人工智能学院，北京，中国）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室，上海，中国）；The University of Tokyo, Tokyo, Japan（东京大学，东京，日本）
Latent Noise Mask for Reducing Visual Redundancy in Multimodal Large Language Models
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院、光学和电子学（iOPEN）、西北工业大学）；Institute of Artificial Intelligence, China Telecom (TeleAI)（人工智能研究院、中国电信（TeleAI））；Fudan University（复旦大学）；The University of Hong Kong（香港大学）
