Medical image retrieval (MIR) is a critical component of computer-aided diagnosis, yet existing systems suffer from three persistent limitations: uniform feature encoding that fails to account for the varying clinical importance of anatomical structures, ambiguous similarity metrics based on coarse classification labels, and an exclusive focus on global image similarity that cannot meet the clinical demand for fine-grained region-specific retrieval. We propose HMAR (Hierarchical Modality-Aware Expert and Dynamic Routing), an adaptive retrieval framework built on a Mixture-of-Experts (MoE) architecture. HMAR employs a dual-expert mechanism: Expert0 extracts global features for holistic similarity matching, while Expert1 learns position-invariant local representations for precise lesion-region retrieval. A two-stage contrastive learning strategy eliminates the need for expensive bounding-box annotations, and a sliding-window matching algorithm enables dense local comparison at inference time. Hash codes are generated via Kolmogorov-Arnold Network (KAN) layers for efficient Hamming-distance search. Experiments on the RadioImageNet-CT dataset (16 clinical patterns, 29,903 images) show that HMAR achieves mean Average Precision (mAP) of 0.711 and 0.724 for 64-bit and 128-bit hash codes, improving over the state-of-the-art ACIR method by 0.7% and 1.1%, respectively.
Hyper-LLaVA: Hyperbolic Uncertainty-aware Modality-Balanced Routing for Multimodal Continual Instruction Tuning
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究所）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Nanjing University of Posts and Telecommunications（南京邮电大学）；Nanjing University Of Aeronautics And Astronautics（南京航空航天大学）；Wuhan University（武汉大学）
Visible-infrared person re-identification (VI Re-ID) aims to match person images between the visible and infrared modalities. Existing VI Re-ID methods mainly focus on extracting homogeneous structural relationships in an image, i.e. the relations between local features, while ignoring the heterogeneous correlation of local features in different modalities. The heterogeneous structured relationship is crucial to learn effective identity representations and perform cross-modality matching. In this paper, we model the homogenous structural relationship by a modality-specific graph within individ…
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs
University of Trento（特伦托大学）；Carnegie Mellon University（卡内基梅隆大学）；LNMIIT Jaipur（贾伊普尔 LNMIIT）；MBZUAI；Sunandan Divatia School of Science（Sunandan Divatia 科学学院）；IIT Bombay（博伊斯大学）
Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics based on the training of data with seen semantics. Recent studies either leverage the global image features or mine discriminative local patch features to associate the extracted visual features to the semantic attributes. However, due to the lack of the necessary top-down guidance and semantic alignment for ensuring the model attending to the real attribute-correlation regions, these methods still encounter a significant semantic gap between the visual modality and the attribute modality, which makes their…
Complementarity-Supervised Spectral-Band Routing for Multimodal Emotion Recognition
Great Bay University（大湾大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；Tencent Youtu Lab（腾讯优图实验室）
SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan College（武汉学院）
Long-Tailed Distribution-Aware Router For Mixture-of-Experts in Large Vision-Language Model
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Electronic and Information, Shanghai Dianji University（上海电机学院电子信息学院）
Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所前沿人工智能研究中心）
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
Fudan University（复旦大学）；Shanghai Innovation Institut（上海创新研究院）；University of Southern California（南加州大学）；Huawei Technologies Co., Ltd（华为技术有限公司）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin, EIT（宁波空间智能与数字衍生关键实验室，数字孪生研究院，EIT）
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
NepAl Applied Mathematics and Informatics Institute（尼泊尔应用数学与信息学研究所）；Fogsphere (Redev.AI Ltd)（福格球（Redev.AI有限公司））；University of Lausanne（洛桑大学）；West Virginia University（西弗吉尼亚大学）；University of Verona（维罗纳大学）；University College London（伦敦大学学院）；University of Aberdeen（阿伯丁大学）
DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis
Department of Systems Engineering, Stevens Institute of Technology（史蒂文斯理工学院系统工程系）；Department of Neurosurgery, Robert Wood Johnson Medical School, Rutgers University（罗格斯大学罗伯特·伍德·约翰逊医学院神经外科）
Lishui University（丽水学院）；Lishui Key Laboratory of Low-Altitude Multimodal Sensing and Intelligent Computing（丽水市低空多模态感知与智能计算重点实验室）；Hangzhou Dianzi University（杭州电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Jiaxing University（嘉兴大学）；Zhejiang University（浙江大学）
The University of Edinburgh（爱丁堡大学）；Imperial College, London（伦敦帝国学院）；University of Surrey（萨里大学）；Queen Mary University of London（伦敦玛丽女王大学）；Microsoft Research, Cambridge（微软研究院剑桥分部）
Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学附属医院）；Hasso Plattner Institute for Digital Engineering（哈索·普拉特纳数字工程研究所）；University of Potsdam（波茨坦大学）；Imperial College London（伦敦帝国理工学院）
Beijing University of Posts and Telecommunications（北京邮电大学）；ByteDance（字节跳动）；USTC（中国科学技术大学）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部互动技术与体验系统重点实验室）；Zhongguancun Academy（中关村科学城）
Northwestern University（西北大学）；Northwestern University Feinberg School of Medicine（西北大学费恩伯格医学院）；Simpson Querrey Lung Institute for Translational Science（辛普森奎雷转化科学肺研究所）
SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation
School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
