A Survey on Video Moment Localization-Reference-Cited by-同舟云学术

A Survey on Video Moment Localization

Published:2023-01-16 Issue:9 Volume:55 Page:1-37
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Liu Meng¹^ORCID,Nie Liqiang²^ORCID,Wang Yunxiao³^ORCID,Wang Meng⁴^ORCID,Rui Yong⁵^ORCID

Affiliation:

1. Shandong Jianzhu University, Jinan, Shandong Province, China

2. Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong Province, China

3. Shandong University, Qingdao, Shandong Province, China

4. Hefei University of Technology, Tunxi Road, Baohe District, Hefei, Anhui Province, China

5. Lenovo Company Ltd., Xibeiwang East Road, Haidian District, Beijing, China

Abstract

Video moment localization, also known as video moment retrieval, aims to search a target segment within a video described by a given natural language query. Beyond the task of temporal action localization whereby the target actions are pre-defined, video moment retrieval can query arbitrary complex activities. In this survey paper, we aim to present a comprehensive review of existing video moment localization techniques, including supervised, weakly supervised, and unsupervised ones. We also review the datasets available for video moment localization and group results of related work. In addition, we discuss promising future directions for this field, in particular large-scale datasets and interpretable video moment localization models.

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation for Distinguished Young Scholars

Major Basic Research Project of Natural Science Foundation of Shandong Province

Science and Technology Innovation Program for Distinguished Young Scholars of Shandong Province Higher Education Institutions

Professors of Shandong Jianzhu University

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3556537

Reference125 articles.

1. Diagnosing Error in Temporal Action Detectors

2. Localizing Moments in Video with Natural Language

3. Weakly Supervised Deep Detection Networks

4. Continuum Robots for Medical Applications: A Survey

5. STRONG: Spatio-Temporal Reinforcement Learning for Cross-Modal Video Moment Localization

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