通过灵巧聚焦改进自视视频中的关键步骤识别
Improving Keystep Recognition in Ego-Video via Dexterous Focus
AI总结：
本文提出一种与网络架构无关的自视视频关键步骤识别框架，通过将输入稳定为以手部为中心的视频，在Ego-Exo4D细粒度基准上超越现有基线，无需改动底层模型。
摘要：
本文研究从第一人称视角理解人类活动所面临的挑战。由于许多活动中头部具有高度动态性，传统活动识别技术在自视视频中遇到独特困难。我们提出一个与网络架构无关的框架，通过将自视视频输入限制为稳定的、以手部为中心的视频来应对这些挑战。我们证明，仅凭这种直接的视频变换，在Ego-Exo4D细粒度关键步骤识别基准上就超越了现有自视视频基线，且无需对底层模型基础设施做任何改动。
英文摘要：
In this paper, we address the challenge of understanding human activities from an egocentric perspective. Traditional activity recognition techniques face unique challenges in egocentric videos due to the highly dynamic nature of the head during many activities. We propose a framework that seeks to address these challenges in a way that is independent of network architecture by restricting the ego-video input to a stabilized, hand-focused video. We demonstrate that this straightforward video transformation alone outperforms existing egocentric video baselines on the Ego-Exo4D Fine-Grained Keystep Recognition benchmark without requiring any alteration of the underlying model infrastructure.
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。
