CLIP4Clip model transferred from the CLIP has been the de-factor standard to solve the video clip retrieval task from frame-level input, triggering the surge of CLIP4Clip-based models in the video-text retrieval domain. In this work, we rethink the inherent limitation of widely-used mean pooling operation in the frame features aggregation and investigate the adaptions of excitation and aggregation design for discriminative video representation generation. We present a novel excitationand-aggregation design, including (1) The excitation module is available for capturing non-mutuallyexclusive relationships among frame features and achieving frame-wise features recalibration, and (2) The aggregation module is applied to learn exclusiveness used for frame representations aggregation. Similarly, we employ the cascade of sequential module and aggregation design to generate discriminative video representation in the sequential type. Besides, we adopt the excitation design in the tight type to obtain representative frame features for multi-modal interaction. The proposed modules are evaluated on three benchmark datasets of MSR-VTT, ActivityNet and DiDeMo, achieving MSR-VTT (43.9 R@1), ActivityNet (44.1 R@1) and DiDeMo (31.0 R@1). They outperform the CLIP4Clip results by +1.2% (+0.5%), +4.5% (+1.9%) and +9.5% (+2.7%) relative (absolute) improvements, demonstrating the superiority of our proposed excitation and aggregation designs. We hope our work will serve as an alternative for frame representations aggregation and facilitate future research.
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Centre for Perceptual and Interactive Intelligence（感知与智能交互中心）
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Hong Kong Polytechnic University（香港理工大学）；Zhuhai Campus of Sun Yat-sen University（中山大学珠海校区）；University of Adelaide（阿德莱德大学）；Peking University（北京大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
The Australian National University（澳大利亚国立大学）；DATA61-CSIRO（澳大利亚联邦科学与工业研究组织数据61部）；NEC Laboratories America（美国电气股份有限公司实验室）；OPPO US Research Center（OPPO美国研究中心）；Monash University（莫纳什大学）
Dual-Hypergraph Indexing: Bridging Knowledge Islands for Multi-Hop Reasoning in Retrieval-Augmented Generation
School of Software Engineering, Xi'an Jiaotong University（西安交通大学软件学院）；EHV Power Transmission Company of China Southern Power Grid Co., Ltd.（中国南方电网有限责任公司超高压输电公司）
Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
KIMIA Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA（KIMIA实验室，人工智能与信息学，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA（神经病学系，北western大学费因伯格医学院，芝加哥，伊利诺伊州，美国）；Department of Computer Science, Temple University, Philadelphia, PA, USA（计算机科学系，泰勒大学，费城，宾夕法尼亚州，美国）；Department of Breast and Melanoma Surgical Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（乳腺和黑色素瘤外科肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA（定量健康科学系，梅奥诊所，罗切斯特，明尼苏达州，美国）
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Usher Institute, Medical School, University of Edinburgh（爱丁堡大学医学院厄舍研究所）
