DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video

• URL: http://arxiv.org/abs/2010.06260v1
• Date: Tue, 13 Oct 2020 09:50:29 GMT
• Title: DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video
• Authors: Cristian Rodriguez-Opazo and Edison Marrese-Taylor and Basura Fernando and Hongdong Li and Stephen Gould
• Abstract summary: We study the task of temporal moment localization in a long untrimmed video using natural language query. Our key innovation is to learn a video feature embedding through a language-conditioned message-passing algorithm. A temporal sub-graph captures the activities within the video through time.
• Score: 98.54696229182335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the task of temporal moment localization in a long untrimmed video using natural language query. Given a query sentence, the goal is to determine the start and end of the relevant segment within the video. Our key innovation is to learn a video feature embedding through a language-conditioned message-passing algorithm suitable for temporal moment localization which captures the relationships between humans, objects and activities in the video. These relationships are obtained by a spatial sub-graph that contextualizes the scene representation using detected objects and human features conditioned in the language query. Moreover, a temporal sub-graph captures the activities within the video through time. Our method is evaluated on three standard benchmark datasets, and we also introduce YouCookII as a new benchmark for this task. Experiments show our method outperforms state-of-the-art methods on these datasets, confirming the effectiveness of our approach.

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