Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a truer, deeper understanding of videos, we introduce the task of win-fail action recognition -- differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," and "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. We systematically analyze the characteristics of the win-fail task/dataset with prototypical action recognition networks and a novel video retrieval task. While current action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from https://github.com/ParitoshParmar/Win-Fail-Action-Recognition.
VA-AR: Learning Velocity-Aware Action Representations with Mixture of Window Attention
Beijing University of Posts and Telecommunications（北京邮电大学）；Macau University of Science and Technology（澳门科技大学）；China Institute of Sport Science（中国体育科学学会）；Beijing Sport University（北京体育大学）
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Institute of Automation, CAS（中国科学院自动化研究所）；DIDI（滴滴）
Text Recognition in Scene Image and Video Frame using Color Channel Selection
Institute of Engineering & Management, Kolkata（加尔各答工程与管理学院）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；Indian Statistical Institute, Kolkata（印度统计学院加尔各答分校）
Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；MThreads, Inc.（MThreads公司）；Nanyang Technological University（南洋理工大学）
