Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications.
EyePCR: A Comprehensive Benchmark for Fine-Grained Perception, Knowledge Comprehension and Clinical Reasoning in Ophthalmic Surgery
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Wenzhou Medical University（温州医科大学）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）
FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding
Lappeenranta-Lahti University of Technology LUT（拉佩兰塔-拉赫蒂技术大学）；Tianjin University（天津大学）；AMD Silo AI；Great Bay University（大湾大学）；Rensselaer Polytechnic Institute（雷士大学）；Brno University of Technology（布拉格技术大学）
Singulos Research Inc.（Singulos研究公司）；University of British Columbia（不列颠哥伦比亚大学）
Augmented and Mixed Reality are emerging as likely successors to the mobile internet. However, many technical challenges remain. One of the key requirements of these systems is the ability to create a continuity between physical and virtual worlds, with the user's visual perception as the primary interface medium. Building this continuity requires the system to develop a visual understanding of the physical world. While there has been significant recent progress in computer vision and AI techniques such as image classification and object detection, success in these areas has not yet led to the…
ScanFormer: Referring Expression Comprehension by Iteratively Scanning
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang-Singapore Innovation and Al Joint Research Lab（浙新创新与人工智能联合研究实验室）
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；TU Delft（代尔夫特理工大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Huawei Zurich Research Center（华为苏黎世研究中心）；Huawei IAS BU（华为IAS业务单元）
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Kuaishou Technology（快手科技）；Huazhong University of Science and Technology（华中科技大学）
SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding
SimulaMet and OsloMet, Norway（SimulaMet和OsloMet，挪威）；Forzasys, Norway（Forzasys，挪威）；SimulaMet, Norway（SimulaMet，挪威）；SimulaMet, OsloMet, and Forzasys, Norway（SimulaMet、OsloMet和Forzasys，挪威）；University of Central Florida, USA（佛罗里达中央大学）
Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging
Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；School of Information and Electronic, Beijing Institute of Technology（北京理工大学信息与电子学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）
本文提出部署于Jetson Orin NX的视觉可穿戴转向辅助系统，通过轻量多任务网络检测跑道线与障碍物，并结合采样和样条曲线规划，帮助视障者安全慢跑。
Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
Institute of Deep Perception Technology, JITRI（JITRI深度感知技术研究所）；University of Liverpool（利物浦大学）；University of Southampton（南安普顿大学）；Momoni AI（Momoni人工智能公司）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Thrust of Artificial Intelligence, HKUST (GZ)（香港科技大学（广州）人工智能学域）；Thrust of Intelligent Transportation, HKUST (GZ)（香港科技大学（广州）智能交通学域）
Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency, high dynamic range, high temporal resolution and low power consumption. Thus, these sensors are suited for robotics and virtual reality applications. To foster the development of 3D perception and navigation algorithms with event cameras, we present the TUM-VIE dataset. It consists of a large variety of handheld and head-mounted sequences in indoor and outdoor environments, including rapid motion during sports and h…
