Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks

Shuangwei Liu, Yunzhou Zhang, Lin Qi, Sonya Coleman, Dermot Kerr, Shangdong Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
16 Downloads (Pure)


The generalization ability of deep person re-identification networks is subject to inadequate person data and occlusions. To relieve this dilemma, we propose a feature-level augmentation strategy, Adversarially Erased Learning Module (AELM), using two adversarial classifiers. Specifically, we utilize a classifier to identify discriminative regions and erase them to increase the variant of features. Meanwhile, we input the erased feature maps to another classifier to discover new body regions, which effectively resist occlusion of key parts. To easily perform end-to-end training for AELM, we propose a novel Identity model based on Fully Convolutional Networks (IFCN) to directly obtain body response heatmap during the forward pass by selecting corresponding class-specific feature map. Thus, the discriminative regions can be identified and erased in a convenient way. Moreover, to capture discriminative region for AELM, we present a Complementary Attention Module (CoAM) combined with channel and spatial attention to automatically focus on which feature types and positions are meaningful in the feature maps. In this paper, CoAM and AELM are cascaded into one module which is applied to the outputs of different convolutional layers to integrate mid- and high-level semantic features. Experimental results on three challenging benchmarks demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
Place of PublicationBudapest
Number of pages9
ISBN (Electronic)9781728119854
Publication statusPublished (in print/issue) - 12 Jul 2019


  • Adversarially Erased Learning
  • Complementary Attention
  • Fully Convolutional Networks


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