computer-vision-1
Temporal Sentence Grounding
3260 papers • 126 benchmarks • 313 datasets
Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. For this task, different levels of supervision are used. 1) Weak supervision: video-level action category set; 2) Semi-weak supervision: video-level action category set, and action annotations at several timestamps; 3) Full supervision: Action category and action interval annotations of all actions in untrimmed videos.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in temporal-sentence-grounding-2
Trend
Dataset
Best Model
Actions
Charades-STA
Charades-STA
Libraries
iUse these libraries to find temporal-sentence-grounding-2 models and implementations
Datasets
Subtasks
No subtasks available.
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.
155