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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3181516
|2 doi
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|a pubmed24n1140.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Liao, Yue
|e verfasserin
|4 aut
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| 245 |
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|a Progressive Language-Customized Visual Feature Learning for One-Stage Visual Grounding
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 01.07.2022
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|a Date Revised 01.07.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Visual grounding is a task to localize an object described by a sentence in an image. Conventional visual grounding methods extract visual and linguistic features isolatedly and then perform cross-modal interaction in a post-fusion manner. We argue that this post-fusion mechanism does not fully utilize the information in two modalities. Instead, it is more desired to perform cross-modal interaction during the extraction process of the visual and linguistic feature. In this paper, we propose a language-customized visual feature learning mechanism where linguistic information guides the extraction of visual feature from the very beginning. We instantiate the mechanism as a one-stage framework named Progressive Language-customized Visual feature learning (PLV). Our proposed PLV consists of a Progressive Language-customized Visual Encoder (PLVE) and a grounding module. We customize the visual feature with linguistic guidance at each stage of the PLVE by Channel-wise Language-guided Interaction Modules (CLIM). Our proposed PLV outperforms conventional state-of-the-art methods with large margins across five visual grounding datasets without pre-training on object detection datasets, while achieving real-time speed. The source code is available in the supplementary material
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|a Journal Article
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| 700 |
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|a Zhang, Aixi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Zhiyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hui, Tianrui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Si
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 16., Seite 4266-4277
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:31
|g year:2022
|g day:16
|g pages:4266-4277
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2022.3181516
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|d 31
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