EgoEsportsQA：一种用于电子竞技感知与推理的视角视频基准
EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports
AI总结：
本文提出EgoEsportsQA基准，通过六阶段流程收集1745对高质量问答对，评估视频大语言模型在虚拟环境中的感知与推理能力，揭示模型在战术推理和微操作方面的不足。
摘要：
尽管视频大语言模型（Video-LLMs）在理解慢节奏的现实视角视频方面表现出色，但其在高速、信息密集的虚拟环境中的能力仍待探索。现有基准主要针对日常活动，缺乏对虚拟场景中快速、规则绑定推理的严格测试平台。为填补这一空白，我们引入EgoEsportsQA，一种开创性的视频问答（QA）基准，用于在专家电子竞技知识中建立感知与推理。我们通过可扩展的六阶段流程，从三大第一人称射击游戏中专业比赛中的1745对高质量QA对。这些问题被划分为二维解耦的分类：11个子任务在认知能力维度（涵盖感知和推理级别）和6个子任务在电子竞技知识维度。对最先进Video-LLMs的全面评估表明，当前模型仍无法达到满意的性能，最佳模型仅71.58%。结果揭示了两个轴上的显著差距：模型在基本视觉感知方面能力更强，但在深度战术推理方面较弱，并且在整体宏观进程上把握得更好，但在细粒度微操作上较弱。广泛的消融实验展示了当前Video-LLM架构的内在弱点。进一步分析表明，我们的数据集不仅揭示了现实世界与虚拟视角领域的联系，还为优化下游电子竞技应用提供了指导，从而促进Video-LLMs在各种视角环境中的未来发展。
英文摘要：
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios. To fill this gap, we introduce EgoEsportsQA, a pioneering video question-answering (QA) benchmark for grounding perception and reasoning in expert esports knowledge. We curate 1,745 high-quality QA pairs from professional matches across 3 first-person shooter games via a scalable six-stage pipeline. These questions are structured into a two-dimensional decoupled taxonomy: 11 sub-tasks in the cognitive capability dimension (covering perception and reasoning levels) and 6 sub-tasks in the esports knowledge dimension. Comprehensive evaluations of state-of-the-art Video-LLMs reveal that current models still fail to achieve satisfactory performance, with the best model only 71.58%. The results expose notable gaps across both axes: models exhibit stronger capabilities in basic visual perception than in deep tactical reasoning, and they grasp overall macro-progression better than fine-grained micro-operations. Extensive ablation experiments demonstrate the intrinsic weaknesses of current Video-LLM architectures. Further analysis suggests that our dataset not only reveals the connections between real-world and virtual egocentric domains, but also offers guidance for optimizing downstream esports applications, thereby fostering the future advancement of Video-LLMs in various egocentric environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
