Egocentric video grounding is a crucial task for embodied intelligence applications, distinct from exocentric video moment localization. Existing methods primarily focus on the distributional differences between egocentric and exocentric videos but often neglect key characteristics of egocentric videos and the fine-grained information emphasized by question-type queries. To address these limitations, we propose OSGNet, an Object-Shot enhanced Grounding Network for egocentric video. Specifically, we extract object information from videos to enrich video representation, particularly for objects highlighted in the textual query but not directly captured in the video features. Additionally, we analyze the frequent shot movements inherent to egocentric videos, leveraging these features to extract the wearer's attention information, which enhances the model's ability to perform modality alignment. Experiments conducted on three datasets demonstrate that OSGNet achieves state-of-the-art performance, validating the effectiveness of our approach. Our code can be found at https://github.com/Yisen-Feng/OSGNet.

本研究针对现有自我中心视频定位方法忽视的自我中心视频特征和细粒度信息的缺失问题，提出了一种新颖的物体镜头增强定位网络（OSGNet）。通过提取视频中的物体信息并分析自我中心视频的镜头运动，本方法显著提升了模型对模态对齐的能力，实验结果表明OSGNet在多个数据集上实现了最先进的性能，证明了该方法的有效性。

面向自我中心视频的物体镜头增强定位网络