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Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Department of Image, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3603;
Received: 18 May 2020 / Revised: 15 June 2020 / Accepted: 24 June 2020 / Published: 26 June 2020
(This article belongs to the Section Intelligent Sensors)
Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets. View Full-Text
Keywords: person re-identification; attention mechanism; Siamese network person re-identification; attention mechanism; Siamese network
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Jeong, D.; Park, H.; Shin, J.; Kang, D.; Paik, J. Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification. Sensors 2020, 20, 3603.

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