通过通用关键帧提取弥合视频问答与视频引导的智能体任务

摘要

视频理解是多模态智能的基本能力，近期多模态大语言模型（MLLMs）在视频问答（VideoQA）基准测试中取得了卓越性能。然而，现有基准主要评估模型能否感知浅层视觉线索，鲜少检验MLLMs能否从视频教程中学习深层知识或程序性技能，并将其泛化至下游长时程智能体任务。为弥补这一空白，我们提出VG-GUIBench（视频引导的图形用户界面基准测试），这一新型基准专门用于评估基于MLLM的GUI智能体是否能遵循视频教程完成相应的GUI交互任务。此外，我们观察到模型在视频问答和视频引导的智能体任务上的表现关键取决于有效的关键帧提取。基于这一发现，我们提出TASKER（任务驱动且场景感知的关键帧搜索算法），该算法联合考虑任务相关性和场景动态性以提取信息帧。实验结果表明，TASKER在视频问答和视频引导的智能体任务基准上均实现了显著性能提升，在EgoSchema完整集和NExT-QA数据集上分别比最优基线高出2.0%和1.8%。这些结果进一步凸显了通用关键帧提取方法在视频理解任务中的潜力。我们的代码和数据详见https://github.com/VG-GUI-TASKER/VG-GUI-TASKER。

English

Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing benchmarks primarily evaluate whether models can perceive shallow visual cues, while rarely examining whether MLLMs can learn deeper knowledge or procedural skills from video tutorials and generalize them to downstream long-horizon agentic tasks. To address this gap, we introduce VG-GUIBench (Video-Guided GUI Benchmark), a new benchmark designed to evaluate whether MLLM-based GUI agents can follow video tutorials to complete corresponding GUI interactive tasks. Furthermore, we observe that the performance of models on both VideoQA and video-guided agentic tasks critically depends on effective keyframe extraction. Based on this observation, we propose TASKER (Task-driven And Scene-aware Keyframe searchER), a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames. Experimental results demonstrate that TASKER achieves significant performance improvements on both VideoQA and video-guided agentic task benchmarks, outperforming the best baseline by 2.0% on the EgoSchema fullset and 1.8% on the NExT-QA dataset, respectively. These results further highlight the potential of generalized keyframe extraction methods for video understanding tasks. Our code and data are available at https://github.com/VG-GUI-TASKER/VG-GUI-TASKER.