标题： FineQuest：通过思维代理推理的自适应知识辅助体育视频理解 显示英文标题

标题： FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning

摘要： 基于大型语言模型（LLMs）的视频问答（VideoQA）在一般的视频理解方面显示出潜力，但在应用于体育视频这一本质上复杂的领域时面临重大挑战。 在本工作中，我们提出了FineQuest，第一个无需训练的框架，该框架借鉴认知科学中的双模式推理：i）反应性推理用于直接的体育查询，ii）反思性推理用于更复杂的查询。 为了弥合通用模型与特定领域体育理解之间的知识差距，FineQuest结合了SSGraph，这是一个跨九种体育项目的多模态体育知识场景图，它编码了视觉实例和领域特定术语以提高推理准确性。 此外，我们引入了两个新的体育VideoQA基准测试，Gym-QA和Diving-QA，它们来源于FineGym和FineDiving数据集，使得评估更加多样和全面。 FineQuest在这些基准测试以及现有的SPORTU数据集上取得了最先进的性能，同时保持了强大的通用VideoQA能力。 显示英文摘要

摘要： Video Question Answering (VideoQA) based on Large Language Models (LLMs) has shown potential in general video understanding but faces significant challenges when applied to the inherently complex domain of sports videos. In this work, we propose FineQuest, the first training-free framework that leverages dual-mode reasoning inspired by cognitive science: i) Reactive Reasoning for straightforward sports queries and ii) Deliberative Reasoning for more complex ones. To bridge the knowledge gap between general-purpose models and domain-specific sports understanding, FineQuest incorporates SSGraph, a multimodal sports knowledge scene graph spanning nine sports, which encodes both visual instances and domain-specific terminology to enhance reasoning accuracy. Furthermore, we introduce two new sports VideoQA benchmarks, Gym-QA and Diving-QA, derived from the FineGym and FineDiving datasets, enabling diverse and comprehensive evaluation. FineQuest achieves state-of-the-art performance on these benchmarks as well as the existing SPORTU dataset, while maintains strong general VideoQA capabilities.