Abstract
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.
Abstract (translated)
尽管具备高级推理能力的视频大语言模型（video-LLMs）取得了快速进展，但先前的研究表明，这些模型在生成体育反馈这一具有挑战性的任务上表现不佳，并且需要昂贵且难以收集的微调数据来针对每项运动进行优化。这种局限性明显体现在模型对微调过程中未见过的运动项目的泛化能力差的问题上。此外，传统的文本生成评估指标（如BLEU-4、METEOR、ROUGE-L和BERTScore），最初为机器翻译和摘要任务设计，无法捕捉体育反馈质量的独特方面。 为了应对第一个问题，我们以攀岩作为案例研究，提出了一种利用目标领域中辅助的免费网络数据的方法，例如比赛视频和教练手册，并结合来自独立源领域的现有体育反馈来提高针对特定运动项目的体育反馈生成性能。为了改进评估方法，我们提出了两个新的评价指标：（1）具体性；（2）可操作性。 我们的方法能够更有效地在注释有限的情况下生成有意义且实用的体育反馈。
URL
https://arxiv.org/abs/2602.08996
