BoxComm：基于 boxing 的类别感知评论生成与叙述节奏基准测试
BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing
AI总结：
本文提出 BoxComm 数据集，包含 445 场世界拳击锦标赛视频及 52000 多条专业评论，通过分类注解和两项新评估方法，解决拳击评论生成中的节奏与类别准确性问题。
摘要：
近年来，多模态大语言模型（MLLMs）在通用视频理解方面表现出色，推动了自动体育评论生成的研究兴趣。然而，现有基准测试仅专注于团队运动，如足球和篮球，忽略了对抗性运动。本文提出 BoxComm 数据集，包含 445 场世界拳击锦标赛视频及 52000 多条专业评论，提出结构化评论分类体系，将每条评论分为实况、战术或上下文类别，为体育评论基准测试提供首个类别级注解。基于此分类体系，本文引入两项新的互补评估方法：（1）类别条件生成，评估模型是否能根据视频内容生成指定类型的评论；（2）评论节奏评估，测量自由生成的评论在连续视频片段中是否具有适当的时间节奏和类型分布，捕捉评论能力的一个新维度。实验表明，当前模型在两项评估中均表现不佳。本文进一步提出 EIC-Gen，通过检测击打事件提供结构化动作提示，取得一致提升，凸显了感知短暂细微事件对拳击评论的重要性。
英文摘要：
Recent multimodal large language models (MLLMs) have shown strong capabilities in general video understanding, driving growing interest in automatic sports commentary generation. However, existing benchmarks for this task focus exclusively on team sports such as soccer and basketball, leaving combat sports entirely unexplored. Notably, combat sports present distinct challenges: critical actions unfold within milliseconds with visually subtle yet semantically decisive differences, and professional commentary contains a substantially higher proportion of tactical analysis compared to team sports. In this paper, we present BoxComm, a large-scale dataset comprising 445 World Boxing Championship match videos with over 52K commentary sentences from professional broadcasts. We propose a structured commentary taxonomy that categorizes each sentence into play-by-play, tactical, or contextual, providing the first category-level annotation for sports commentary benchmarks. Building on this taxonomy, we introduce two novel and complementary evaluations tailored to sports commentary generation: (1) category-conditioned generation, which evaluates whether models can produce accurate commentary of a specified type given video context; and (2) commentary rhythm assessment, which measures whether freely generated commentary exhibits appropriate temporal pacing and type distribution over continuous video segments, capturing a dimension of commentary competence that prior benchmarks have not addressed. Experiments on multiple state-of-the-art MLLMs reveal that current models struggle on both evaluations. We further propose EIC-Gen, an improved baseline incorporating detected punch events to supply structured action cues, yielding consistent gains and highlighting the importance of perceiving fleeting and subtle events for combat sports commentary.
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。
