Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights
AI 导读
语义动作图代理生成人类解释体育视频分析结构化表示
文章提出语义动作图，通过结构化表示解决AI生成体育视频亮点的可解释性问题，同时支持人类查询与分析。
This paper introduces a semantic action graph to address AI-generated sports highlights' interpretability, enabling both agent grounding and human analysis through structured representation.
重点速览
- 提出语义动作图作为轻量级领域模式，整合表演者、动作、时刻等要素 Introduces semantic action graph as a lightweight domain schema integrating performers, actions, and moments
- 三重核心属性：事件序列连接、共享词汇、可定位帧级时刻 Three core properties: connected event sequences, shared vocabulary, and frame-addressable moments
- SportSAGE系统验证显示用户可有效通过图界面查询视频亮点 SportSAGE system validation shows users can effectively query highlights via graph interface
- 解决生成式代理输出难以验证与个性化调整的行业痛点 Addresses the industry challenge of verifying and personalizing AI-generated content
一句话：结构化语义框架可同时满足AI生成与人类交互需求 / Structured semantic framework satisfies both AI generation and human interaction needs
推荐理由 提供AI与用户交互的新范式，对体育内容生成有实践指导价值
二次创作声明：本页为 AI 热榜聚合导读，内容与热度数据来自公开来源 （arxiv），版权归原始作者所有；本站仅做转载指引与摘要评述，不复制原文。