video-retrieval
Video Grounding
3260 papers • 126 benchmarks • 313 datasets
Video grounding is the task of linking spoken language descriptions to specific video segments. In video grounding, the model is given a video and a natural language description, such as a sentence or a caption, and its goal is to identify the specific segment of the video that corresponds to the description. This can involve tasks such as localizing the objects or actions mentioned in the description within the video, or associating a specific time interval with the description.
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These leaderboards are used to track progress in video-retrieval
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QVHighlights
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MAD
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iUse these libraries to find video-retrieval models and implementations
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Most implemented papers
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
Limin Wang, Zhenzhi Wang, Gangshan Wu, Tao Wu, Tianhao Li•Thu Sep 09 2021
A Mutual Matching Network (MMN) is presented, to directly model the similarity between language queries and video moments in a joint embedding space and suggests that metric learning is still a promising method for temporal grounding via capturing the essential cross-modal correlation in aointing space.
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