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|a 10.1109/TIP.2021.3090521
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|a eng
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|a Hu, Yupeng
|e verfasserin
|4 aut
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|a Coarse-to-Fine Semantic Alignment for Cross-Modal Moment Localization
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|c 2021
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|a Text
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|a Date Revised 30.06.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors
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|a Journal Article
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|a Nie, Liqiang
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|a Liu, Meng
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| 700 |
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|a Wang, Kun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yinglong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hua, Xian-Sheng
|e verfasserin
|4 aut
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|g 30(2021) vom: 24., Seite 5933-5943
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|u http://dx.doi.org/10.1109/TIP.2021.3090521
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