CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval

• URL: http://arxiv.org/abs/2109.10016v1
• Date: Tue, 21 Sep 2021 08:07:27 GMT
• Title: CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval
• Authors: Zhijian Hou, Chong-Wah Ngo, Wing Kwong Chan
• Abstract summary: Video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel model for effective moment localization and ranking. We conduct studies on two datasets, TVR for closed-world TV episodes and DiDeMo for open-world user-generated videos.
• Score: 24.649068267308913
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper tackles a recently proposed Video Corpus Moment Retrieval task. This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel CONtextual QUery-awarE Ranking~(CONQUER) model for effective moment localization and ranking. CONQUER explores query context for multi-modal fusion and representation learning in two different steps. The first step derives fusion weights for the adaptive combination of multi-modal video content. The second step performs bi-directional attention to tightly couple video and query as a single joint representation for moment localization. As query context is fully engaged in video representation learning, from feature fusion to transformation, the resulting feature is user-centered and has a larger capacity in capturing multi-modal signals specific to query. We conduct studies on two datasets, TVR for closed-world TV episodes and DiDeMo for open-world user-generated videos, to investigate the potential advantages of fusing video and query online as a joint representation for moment retrieval.

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