Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2021

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.

Keywords

cross-modal retrieval, moment localization with natural language

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 29th ACM International Conference on Multimedia, MM 2021, Virtual Conference, 2021 October 20-24

First Page

3900

Last Page

3908

ISBN

9781450386517

Identifier

10.1145/3474085.3475281

Publisher

Association for Computing Machinery, Inc

City or Country

Virtual Conference

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