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Joint learning dynamic pruning and attention for person re-identification

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Abstract

In this paper, we investigate the problem of person re-identification by learning pedestrian distinguishing features and reducing model complexity. Traditional methods usually extract pedestrian features by designing better network structures and loss functions, which lack the consideration of the model size and ignore the impact of model efficiency on the accuracy of person re-identification. In this work, an end-to-end joint learning framework, namely PA-Net, with attention model and dynamic filter pruning algorithm is proposed. First, for a feature node, we mine patterns from a compact representation for attention learning, which points out the direction for dynamic filter pruning during training. The compact representation is obtained by stacking its pairwise relations with all feature nodes as a vector. Second, in an epoch training phase, the filters of small ℓ2-norm are given high priority of being pruned to temporarily eliminate their contribution to the model output than those of higher ℓ2-norm. Pruned filters can still be updated in the next epoch training phase until some filters no longer have any effect on the model and are completely pruned. Third, the weighted regularized triplet (WRT) loss and center loss are used to constrain the original features, and the softmax loss is used to constrain the batch normalized (BN) processed features to obtain the final score. Comprehensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets clearly show the superior performance of our proposed method in comparison with state-of-the-art methods.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Ru Cheng & Mingrun Wei

  2. College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an, 271000, China

    Lukun Wang & Chunpeng Tian

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  1. Ru Cheng
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  2. Lukun Wang
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  4. Chunpeng Tian
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Correspondence to Lukun Wang.

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Cheng, R., Wang, L., Wei, M. et al. Joint learning dynamic pruning and attention for person re-identification. Multimed Tools Appl 81, 39409–39429 (2022). https://doi.org/10.1007/s11042-022-12195-6

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