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Multi-scale attention vehicle re-identification

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Abstract

Vehicle re-identification (Re-ID) aims to match the vehicle images with the same identity captured by the non-overlapping surveillance cameras. Most existing vehicle Re-ID methods focus on effective deep network architectures to extract discriminative features from single-scale images. However, these methods ignored the complementary information from different scales, which is a crucial factor in computer vision tasks. Attention mechanism, a commonly used technique in recognition and detection tasks, can selectively focus on discriminative local cues of the image. In this work, we propose a multi-scale attention framework which jointly considers multi-scale mechanism and attention technique for vehicle Re-ID. Specifically, we exploit multi-scale mechanism in feature maps, which can acquire more comprehensive representations for fusing global and local cues. Meanwhile, we exploit attention blocks on each scale subnetwork, which aims to mine complementary and discriminative information. We conduct extensive experiments on three vehicle datasets, VeRi-776, VehicleID and PKU-VD. The promising results demonstrate the effectiveness of the proposed method and yield to a new state of the art for vehicle Re-ID.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61976002, 61860206004), the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2019A0033), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201900046).

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  1. Xianmin Lin and Jiacheng Dong have been contributed equally to this paper.

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Aihua Zheng, Xianmin Lin, Jiacheng Dong, Wenzhong Wang, Jin Tang & Bin Luo

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  1. Aihua Zheng
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  2. Xianmin Lin
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  3. Jiacheng Dong
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  4. Wenzhong Wang
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Correspondence to Wenzhong Wang.

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Zheng, A., Lin, X., Dong, J. et al. Multi-scale attention vehicle re-identification. Neural Comput & Applic 32, 17489–17503 (2020). https://doi.org/10.1007/s00521-020-05108-x

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