Mid-level features based on visual dictionaries are today a cornerstone of systems for classification and retrieval of images. Those state-of-the-art representations depend crucially on the choice of a codebook (visual dictionary), which is usually derived from the dataset. In general-purpose, dynamic image collections (e.g., the Web), one cannot have the entire collection in order to extract a representative dictionary. However, based on the hypothesis that the dictionary reflects only the diversity of low-level appearances and does not capture semantics, we argue that a dictionary based on a small subset of the data, or even on an entirely different dataset, is able to produce a good representation, provided that the chosen images span a diverse enough portion of the low-level feature space. Our experiments confirm that hypothesis, opening the opportunity to greatly alleviate the burden in generating the codebook, and confirming the feasibility of employing visual dictionaries in large-scale dynamic environments.
Scale-Aware Pre-Training for Human-Centric Visual Perception: Enabling Lightweight and Generalizable Models
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（深圳先进研究院，电子科学与技术大学）；Center for Future Media, University of Electronic Science and Technology of China（未来媒体中心，电子科学与技术大学）；TongJi University（同济大学）
Semantic Diversity versus Visual Diversity in Visual Dictionaries
SAMSUNG Research Institute（三星研究院）；School of Electrical and Computing Engineering, University of Campinas (Unicamp)（坎皮纳斯大学（Unicamp）电气与计算机工程学院）；Institute of Computing, University of Campinas (Unicamp)（坎皮纳斯大学（Unicamp）计算研究所）
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
Tsinghua University（清华大学）；State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qizhi Institute（上海期智研究院）；Department of Mechanical Engineering, Tsinghua University（清华大学机械工程系）；Northwestern Polytechnical University（西北工业大学）；Center on Frontiers of Computing Studies, Peking University（北京大学计算与前沿研究中心）；Shanghai AI Laboratory（上海人工智能实验室）
VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
National University of Singapore（新加坡国立大学）；Australian National University（澳大利亚国立大学）；New York University（纽约大学）；The University of New South Wales（新南威尔士大学）
Do You See What I Say? Generalizable Deepfake Detection based on Visual Speech Recognition
Machine Intelligence Group, Department of CS&IS, Birla Institute of Technology and Sciences, Pilani, India（机器智能组，计算机科学与信息系，比拉理工学院和科学学院，比拉，印度）；STARS team Inria Center at Université Côte d’Azur in Sophia Antipolis, France（法国索菲亚Antipolis大学的Inria中心的STARS团队）
Beyond Semantic Priors: Mitigating Optimization Collapse for Generalizable Visual Forensics
Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学信息与科学学院）
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
School of Informatics, Xiamen University（厦门大学信息学院）；Huawei（华为）；Aginome Scientific（Aginome科技公司）；Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属胸科医院）
针对 WSI 分类中粗粒度语义监督不足和高分辨率计算开销问题，提出 FiVE 框架，通过细粒度病理语义交互、TFS 模块和实例子集采样提升泛化能力。
Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The First Affiliated Hospital, Sun Yat-sen University（中山大学第一附属医院）；Centre for Artificial Intelligence and Robotics, Chinese Academy of Sciences, HK, China（中国科学院香港人工智能与机器人中心）；School of Engineering and Imaging Sciences, King’s College London, UK（伦敦国王学院工程与影像科学学院）
MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)（计算机科学与技术学院，齐鲁工业大学（山东省科学院））；School of Computing, National University of Singapore（国立新加坡大学计算机学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东省人工智能研究院，齐鲁工业大学（山东省科学院））；Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology（教育部计算机视觉与系统重点实验室，天津工业大学）
Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of New Networks, Pengcheng Laboratory（鹏城实验室网络部）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络安全科学与技术学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；School of Cyberspace Science, Harbin Institute of Technology（哈尔滨工业大学网络空间科学学院）
