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Person re-identification based on multi-scale constraint network
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DOI：10.1016/j.patrec.2020.08.012.png)
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Abstract
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Combining features of different scales to learn a more discriminative model is an essential solution for person re-identification (Re-ID) tasks. Most existing multi-scale methods are based on the fusion of features from different scales, which cannot exploit information throughly at each scale and cause gradient chaos in optimizing. To address this problem, in this paper we propose an end-to-end multi-scale constraint network(MSCN) to capture detailed information from multiple scales which can independently train each scale and integrate the features of each scale for prediction. In order to retain more information at different scales, we uniformly divide the feature maps into several parts, and vary the number of parts in different scales, then concatenate all the parts in each scale as the entire feature for training. We use both classification loss and metric loss to optimize the network from different aspects. Extensive experiments on three datasets demonstrate that our method achieves very competitive performance. Especially on the CUHK03 dataset, our approach achieves the state-of-the-art results outperforming the current best method by 2.4%/2.0% in Rank-1/mAP. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Multi-scale
Person Re-ID
TriHard loss
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