A Cross-modal Person Re-identification Method Based on Hybrid Channel Augmentation with Structured Dual Attention

ZHUANG Jianjun; ZHUANG Yuchen

doi:10.11999/JEIT230614

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 518-526

ZHUANG Jianjun, ZHUANG Yuchen. A Cross-modal Person Re-identification Method Based on Hybrid Channel Augmentation with Structured Dual Attention[J]. Journal of Electronics & Information Technology, 2024, 46(2): 518-526. doi: 10.11999/JEIT230614

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ZHUANG Jianjun, ZHUANG Yuchen. A Cross-modal Person Re-identification Method Based on Hybrid Channel Augmentation with Structured Dual Attention[J]. Journal of Electronics & Information Technology, 2024, 46(2): 518-526. doi: 10.11999/JEIT230614

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A Cross-modal Person Re-identification Method Based on Hybrid Channel Augmentation with Structured Dual Attention

doi: 10.11999/JEIT230614 cstr: 32379.14.JEIT230614

ZHUANG Jianjun^{1, 2
,
,},
ZHUANG Yuchen¹

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Key Research and Development Program (2021YFE0105500), The National Natural Science Foundation of China (62171228), Jiangsu Qinglan Project

Received Date: 2023-06-21
Accepted Date: 2023-11-14
Rev Recd Date: 2023-11-03

Available Online: 2023-11-17

Publish Date: 2024-02-29

Abstract

Abstract

In the current research on cross-modal person re-identification technology, most existing methods reduce cross-modal differences by using single modal original visible light images or locally shared features of adversarially generated images, resulting in a lack of stable recognition accuracy in infrared image discrimination due to the loss of feature information. In order to solve this problem, A cross-modal person re-identification method based on swappable hybrid random channel augmentation with structured dual attention is proposed. The visual image after channel enhancement is used as the third mode, and the single channel and three channels random hybrid enhancement extraction of visible image is performed through the Image Channel Swappable random mix Augmentation (I-CSA) module, so as to highlight the structural details of pedestrian posture, Reduce modal differences in learning. The Structured joint Attention Feature Fusion (SAFF) module provides richer supervision for cross-modal Feature learning, and enhances the robustness of shared features in modal changes, under the premise of focusing on the structural relationship of pedestrian attitudes between modes. Under the single shot setting of full search mode in the SYSU-MM01 dataset, Rank-1 and mAP reached 71.2% and 68.1%, respectively, surpassing similar cutting-edge methods.
- Person Re-identification,
- Cross-modal,
- Hybrid channel enhancement,
- Joint attention,
- Feature fusion

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References(27)

References

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