BFMD：一款完整的羽毛球密集数据集用于密集击球标注
BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning
AI总结：
本文提出BFMD数据集，包含19场完整比赛，提供密集多模态标注，采用VideoMAE框架结合语义反馈机制提升击球标注质量。
摘要：
理解羽毛球战术动态需要分析完整比赛而非孤立片段。现有数据集主要关注短片段或特定任务标注，缺乏完整比赛的密集多模态标注。为此，我们引入首个Badminton Full Match Dense（BFMD）数据集，包含19场比赛，涵盖超过20小时的比赛，包含1,687次击球和16,751个击球事件，每个事件均标注有击球描述。数据集提供层次化标注，包括比赛片段、击球事件和密集的击球级多模态标注，如击球类型、球轨迹、球员姿态关键点和击球描述。我们开发了基于VideoMAE的多模态标注框架，结合语义反馈机制，利用击球语义引导标注生成并提高语义一致性。实验结果表明，多模态建模和语义反馈在RGB-only基线基础上提升了击球标注质量。我们进一步通过分析完整比赛中的战术模式时间演变展示了BFMD的潜力。
英文摘要：
Understanding tactical dynamics in badminton requires analyzing entire matches rather than isolated clips. However, existing badminton datasets mainly focus on short clips or task-specific annotations and rarely provide full-match data with dense multimodal annotations. This limitation makes it difficult to generate accurate shot captions and perform match-level analysis. To address this limitation, we introduce the first Badminton Full Match Dense (BFMD) dataset, with 19 broadcast matches (including both singles and doubles) covering over 20 hours of play, comprising 1,687 rallies and 16,751 hit events, each annotated with a shot caption. The dataset provides hierarchical annotations including match segments, rally events, and dense rally-level multimodal annotations such as shot types, shuttle trajectories, player pose keypoints, and shot captions. We develop a VideoMAE-based multimodal captioning framework with a Semantic Feedback mechanism that leverages shot semantics to guide caption generation and improve semantic consistency. Experimental results demonstrate that multimodal modeling and semantic feedback improve shot caption quality over RGB-only baselines. We further showcase the potential of BFMD by analyzing the temporal evolution of tactical patterns across full matches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
