用大模型生成专业风格体育赛事描述，准确率提升超10%。
Large VLM-based Stylized Sports Captioning
- 分两阶段微调视觉语言模型，强化体育术语理解
- F1得分提升8-10%，BERT分数提升2-10%
- 适合实时体育直播内容生成，运行高效
大型视觉语言模型（LVLM）在社交媒体、推荐系统等领域广泛应用，但在体育领域仍存在不足：现有模型难以准确识别比赛动作并生成自然流畅的描述。本文指出当前主流模型在体育场景下缺乏专业术语表达能力，提出一种两级微调的LVLM流水线，显著提升生成质量。实验显示，该方法在F1指标上优于基准方案8-10%，在BERT评分上提升2-10%。模型内存占用小，执行速度快。在超级碗LIX比赛中，该系统以每3-5秒处理6张图像的速度，为超过1000张图像生成了高精度、风格化的实时解说文案，验证了其实际应用价值。
原文摘要 · Abstract (English)
The advent of large (visual) language models (LLM / LVLM) have led to a deluge of automated human-like systems in several domains including social media content generation, search and recommendation, healthcare prognosis, AI assistants for cognitive tasks etc. Although these systems have been successfully integrated in production; very little focus has been placed on sports, particularly accurate identification and natural language description of the game play. Most existing LLM/LVLMs can explain generic sports activities, but lack sufficient domain-centric sports' jargon to create natural (human-like) descriptions. This work highlights the limitations of existing SoTA LLM/LVLMs for generating production-grade sports captions from images in a desired stylized format, and proposes a two-level fine-tuned LVLM pipeline to address that. The proposed pipeline yields an improvement > 8-10% in the F1, and > 2-10% in BERT score compared to alternative approaches. In addition, it has a small runtime memory footprint and fast execution time. During Super Bowl LIX the pipeline proved its practical application for live professional sports journalism; generating highly accurate and stylized captions at the rate of 6 images per 3-5 seconds for over 1000 images during the game play.
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