Further Non-Local and Channel Attention Networks for Vehicle Re-Identification

Kai Liu, Zheng Xu, Zhaohui Hou, Zhicheng Zhao, Fei Su; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 584-585

Abstract


Vehicle re-identification remains challenging due to large intra-class difference and small inter-class variance. To address this problem, in AICity Vehicle Re-ID task 2020, we propose a two-branch adaptive attention network--Further Non-local and Channel attention (FNC) to improve feature representation and discrimination. Specifically, inspired by two-stream theory of visual cortex, based on Non-local and channel relation, a two-branch FNC network is constructed to capture multiple useful information. Second, an effective attention fusion method is proposed to sufficiently model the effects from spatial and channel attention. The experimental results show that our algorithm achieves 66.25%/Rank-1 and 53.54%/mAP in 2020 AICity Challenge Vehicle Re-ID task without using extra data, annotation and other auxiliary information, which demonstrate the effectiveness of the proposed FNC network.

Related Material


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[bibtex]
@InProceedings{Liu_2020_CVPR_Workshops,
author = {Liu, Kai and Xu, Zheng and Hou, Zhaohui and Zhao, Zhicheng and Su, Fei},
title = {Further Non-Local and Channel Attention Networks for Vehicle Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}