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arXiv · 1807.11182

End-to-End Deep Kronecker-Product Matching for Person Re-identification

Yantao Shen·Tong Xiao·Hongsheng Li·Shuai Yi·Xiaogang Wang
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Abstract

Person re-identification aims to robustly measure similarities between person images. The significant variation of person poses and viewing angles challenges for accurate person re-identification. The spatial layout and correspondences between query person images are vital information for tackling this problem but are ignored by most state-of-the-art methods. In this paper, we propose a novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network. A novel feature soft warping scheme is designed for aligning the feature maps based on matching results, which is shown to be crucial for achieving superior accuracy. The multi-scale features based on hourglass-like networks and self-residual attention are also exploited to further boost the re-identification performance. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach.

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Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang. 2018-07-30. End-to-End Deep Kronecker-Product Matching for Person Re-identification. https://arxiv.org/abs/1807.11182

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