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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3087348
|2 doi
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|a pubmed24n1089.xml
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Yu, Zitong
|e verfasserin
|4 aut
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| 245 |
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|a Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Completed 21.06.2021
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|a Date Revised 21.06.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Gesture recognition has attracted considerable attention owing to its great potential in applications. Although the great progress has been made recently in multi-modal learning methods, existing methods still lack effective integration to fully explore synergies among spatio-temporal modalities effectively for gesture recognition. The problems are partially due to the fact that the existing manually designed network architectures have low efficiency in the joint learning of multi-modalities. In this paper, we propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and 2) optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities. The resultant multi-modal multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics. Comprehensive experiments are performed on three benchmark datasets (IsoGD, NvGesture, and EgoGesture), demonstrating the state-of-the-art performance in both single- and multi-modality settings. The code is available at https://github.com/ZitongYu/3DCDC-NAS
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|a Journal Article
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1 |
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|a Zhou, Benjia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wan, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Pichao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Haoyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Stan Z
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Guoying
|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 30(2021) vom: 14., Seite 5626-5640
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|g volume:30
|g year:2021
|g day:14
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|u http://dx.doi.org/10.1109/TIP.2021.3087348
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