Title | ||
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Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding. |
Abstract | ||
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Temporal language grounding (TLG) aims to localize a video segment in an untrimmed video based on a natural language description. To alleviate the expensive cost of manual annotations for temporal boundary labels, we are dedicated to the weakly supervised setting, where only video-level descriptions are provided for training. Most of the existing weakly supervised methods generate a candidate segment set and learn cross-modal alignment through a MIL-based framework. However, the temporal structure of the video as well as the complicated semantics in the sentence are lost during the learning. In this work, we propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG. Instead of view the sentence and candidate moments as a whole, FSAN learns token-by-clip cross-modal semantic alignment by an iterative cross-modal interaction module, generates a fine-grained cross-modal semantic alignment map, and performs grounding directly on top of the map. Extensive experiments are conducted on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo, where our FSAN achieves state-of-the-art performance. |
Year | DOI | Venue |
---|---|---|
2021 | 10.18653/v1/2021.findings-emnlp.9 | EMNLP |
DocType | Volume | Citations |
Conference | 2021.findings-emnlp | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yuechen Wang | 1 | 0 | 0.34 |
Wengang Zhou | 2 | 1226 | 79.31 |
Houqiang Li | 3 | 2090 | 172.30 |