ABD-Net: Attentive but Diverse Person Re-Identification

Tianlong Chen, Shaojin Ding, Jingyi Xie, Ye Yuan, Wuyang Chen, Yang Yang, Zhou Ren, Zhangyang Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 8351-8361


Attention mechanisms have been found effective for person re-identification (Re-ID). However, the learned "attentive" features are often not naturally uncorrelated or "diverse", which compromises the retrieval performance based on the Euclidean distance. We advocate the complementary powers of attention and diversity for Re-ID, by proposing an Attentive but Diverse Network (ABD-Net). ABD-Net seamlessly integrates attention modules and diversity regularizations throughout the entire network to learn features that are representative, robust, and more discriminative. Specifically, we introduce a pair of complementary attention modules, focusing on channel aggregation and position awareness, respectively. Then, we plug in a novel orthogonality constraint that efficiently enforces diversity on both hidden activations and weights. Through an extensive set of ablation study, we verify that the attentive and diverse terms each contributes to the performance boosts of ABD-Net. It consistently outperforms existing state-of-the-art methods on there popular person Re-ID benchmarks.

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[pdf] [supp]
author = {Chen, Tianlong and Ding, Shaojin and Xie, Jingyi and Yuan, Ye and Chen, Wuyang and Yang, Yang and Ren, Zhou and Wang, Zhangyang},
title = {ABD-Net: Attentive but Diverse Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}