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- W4372190174 abstract "Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed." @default.
- W4372190174 created "2023-05-07" @default.
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- W4372190174 date "2023-06-04" @default.
- W4372190174 modified "2023-10-16" @default.
- W4372190174 title "Tracking Objects and Activities with Attention for Temporal Sentence Grounding" @default.
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- W4372190174 doi "https://doi.org/10.1109/icassp49357.2023.10096206" @default.
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