Frame quality deterioration is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration modules to obtain spatio-temporal information. However, these integration modules are heavy and complex. Furthermore, each integration module is specifically tailored for its target task, making it difficult to generalise to multiple tasks. In this paper, we present a neat and unified framework, called Spatio-Temporal Prompting Network (STPN). It can efficiently extract robust and accurate video features by dynamically adjusting the input features in the backbone network. Specifically, STPN predicts several video prompts containing spatio-temporal information of neighbour frames. Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction. Moreover, STPN is easy to generalise to various video tasks because it does not contain task-specific modules. Without bells and whistles, STPN achieves state-of-the-art performance on three widely-used datasets for different video understanding tasks, i.e., ImageNetVID for video object detection, YouTubeVIS for video instance segmentation, and GOT-10k for visual object tracking. Code is available at https://github.com/guanxiongsun/vfe.pytorch.
From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Technical University of Munich（慕尼黑技术大学）；Korea University（韩国大学）；Kyung Hee University（庆熙大学）；Seoul National University of Science and Technology（首尔科学技术大学）
State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；the Huazhong University of Science and Technology（华中科技大学）；Amazon.com, Inc（亚马逊公司）
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Emory University（埃默里大学）；Tongji University（同济大学）；The Hong Kong University of Science and Technology（香港科技大学）；Georgia Institute of Technology（佐治亚理工学院）；Northwestern University（西北大学）
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Auto-Prompting SAM for Weakly Supervised Landslide Extraction
School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理学院）；Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（慕尼黑技术大学地球观测数据科学教授职位）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克亥姆霍兹研究中心）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）
DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Department of Computer Science and Engineering, University of North Texas（北卡罗来纳州立大学计算机科学与工程系）；OPPO AI Center（OPPO人工智能中心）
DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；Second Research Institute of Civil Aviation Administration of China（中国民用航空局第二研究所）
Medal S: Spatio-Textual Prompt Model for Medical Segmentation
Medical Image Insights, Shanghai, China（医学影像洞察，上海，中国）；University of Washington, Seattle, WA, USA（华盛顿大学，西雅图，华盛顿州，美国）；University of Waterloo, Waterloo, ON, Canada（滑铁卢大学，滑铁卢，安大略省，加拿大）；Xi'an Jiaotong University, Xi'an, China（西安交通大学，西安，中国）
STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification
University of Michigan（密歇根大学）
我们考虑将KronPCA时空建模技术[Greenewald et al 2013，Tsiligkarkal et al 2013]应用于提取用于视频分类的时空特征。KronPCA执行低秩类型的降维，该降维适用于时空数据，其特征在于T帧多帧均值和协方差的p空间特征。为了进一步正则化和改进逆估计，我们还使用了[Greenewald et al 2013]中提出的对角校正KronPCA收缩方法。我们将这种非常通用的方法应用于从视频中的行人边界框提取的HOG特征的多变量时间行为建模，并选择具有挑战性的数据集中的性别分类作为特定应用。每个类的学习协方差用于提取时空特征，然后进行分类，实现有竞争力的分类性能。
Video-Level Language-Driven Video-Based Visible-Infrared Person Re-Identification
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Chongqing Institute for Brain and Intelligence（重庆脑科学与智能技术研究院）；Guangyang Bay Laboratory（广阳湾实验室）
End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting
Beijing Institute of Technology（北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；University of Rochester（罗切斯特大学）
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；University of Applied Sciences and Arts Western Switzerland（瑞士西部应用科学与艺术大学）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）；Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Institute of Mathematics for Industry, Kyushu University（九州大学产业数学研究所）
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Continuing Education, Shanghai Jiao Tong University（上海交通大学继续教育学院）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）；Center for Advanced Neuroimaging, University of California, Riverside（加州大学河滨分校高级神经影像中心）
