用比赛视频和教练手册提升攀岩反馈生成，解决数据少难标注问题。
Generalizing Sports Feedback Generation by Watching Competitions and Reading Books: A Rock Climbing Case Study
- 融合攀岩比赛视频与教练手册，补充训练数据
- 新指标提升反馈质量评估准确性，超越传统方法
- 适合缺乏标注数据的体育领域反馈生成研究
尽管视频大模型在推理能力上进展迅速，但在体育反馈生成任务中仍表现不佳，且需为每项运动收集昂贵、难以获取的微调数据，导致对未见运动泛化能力差。传统文本评价指标（如BLEU-4、METEOR、ROUGE-L、BERTScore）原本用于机器翻译与摘要，无法捕捉体育反馈的独特质量特征。为此，以攀岩为例，我们提出利用目标领域免费可得的网络数据（如竞赛视频、教练手册），结合来自非重叠源领域的现有体育反馈，提升目标领域反馈生成性能。同时，提出两个新评估指标：特异性与可操作性。该方法在标注数据有限的情况下，实现了更真实、更具实用价值的体育反馈生成。
原文摘要 · Abstract (English)
While there is rapid progress in video-LLMs with advanced reasoning capabilities, prior work shows that these models struggle on the challenging task of sports feedback generation and require expensive and difficult-to-collect finetuning feedback data for each sport. This limitation is evident from the poor generalization to sports unseen during finetuning. Furthermore, traditional text generation evaluation metrics (e.g., BLEU-4, METEOR, ROUGE-L, BERTScore), originally developed for machine translation and summarization, fail to capture the unique aspects of sports feedback quality. To address the first problem, using rock climbing as our case study, we propose using auxiliary freely-available web data from the target domain, such as competition videos and coaching manuals, in addition to existing sports feedback from a disjoint, source domain to improve sports feedback generation performance on the target domain. To improve evaluation, we propose two evaluation metrics: (1) specificity and (2) actionability. Together, our approach enables more meaningful and practical generation of sports feedback under limited annotations.
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