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Temporal Sentence Grounding in Videos: A Survey and Future Directions

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arxiv 2201.08071 v3 pith:JGBFERRT submitted 2022-01-20 cs.CV cs.AIcs.CLcs.MM

Hao Zhang , Aixin Sun , Wei Jing , Joey Tianyi Zhou This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.MM
keywords tsgvlanguageresearchvideodirectionsmomenttemporalcurrent
verification ladder T0 review T1 audit T2 compute T3 formal
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Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate the methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection

    cs.CV 2025-01 conditional novelty 4.0 of 10

    MRNet fuses RGB, optical flow, and depth features with word-, phrase-, and sentence-level query features, and reports improved moment retrieval and highlight detection scores on QVHighlights and Charades-STA.

  2. Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models

    cs.CV 2024-12 reject novelty 4.0 of 10

    The paper claims that a video-language model trained with dynamic temporal prompts and temporal contrastive learning beats four published models on three self-defined VidSitu temporal reasoning tasks.

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