mlsanityData to 7 Oct 2026

A Survey on Natural Language Video Localization

arXiv 2104.00234 ↗First posted 1 Apr 2021cs.CV cs.MMcited by 6

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Abstract (arXiv)

Natural language video localization (NLVL), which aims to locate a target moment from a video that semantically corresponds to a text query, is a novel and challenging task. Toward this end, in this paper, we present a comprehensive survey of the NLVL algorithms, where we first propose the pipeline of NLVL, and then categorize them into supervised and weakly-supervised methods, following by the analysis of the strengths and weaknesses of each kind of methods. Subsequently, we present the dataset, evaluation protocols and the general performance analysis. Finally, the possible perspectives are obtained by summarizing the existing methods.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 59 of its 101 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

59 of 101 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

6 papers.

  1. 2022Temporal Sentence Grounding in Videos: A Survey and Future Directionscited by 37
  2. 2021A Survey on Temporal Sentence Grounding in Videoscited by 29
  3. 2022Video Moment Retrieval from Text Queries via Single Frame Annotationcited by 19
  4. 2025A Survey on Video Temporal Grounding with Multimodal Large Language Modelcited by 19
  5. 2023A Survey on Video Moment Localizationcited by 18
  6. 2026HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoningcited by 0