ABD-Net: Attentive but Diverse Person Re-Identification

By Tianlong Chen, Shaojin Ding, Jingyi Xie, Ye Yuan, Wuyang Chen, Zhou Ren, Zhangyang Wang. IEEE International Conference on Computer Vision, 2019

555 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/17adb315d1fdc0757dae5d72db4a66143102d1c3

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Learning for Person Re-Identification: A Survey and Outlook (2020, methodology): “We also compare two state-of-the-art methods, BagTricks [151] and ABD-Net [137].”
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels (2022, methodology): “ABDNet (Chen et al. 2019) adopts a similar attention module and adds a regularization term to ensure feature orthogonality.”
Attention mechanisms in computer vision: A survey (2021, unknown): “[130, 101, 123, 86, 96, 17, 84], [108, 40, 61, 166, 148, 152, 135] , [122, 14, 19, 62, 78]”
Domain Generalization: A Survey (2021, methodology): “A common practice is to combine several face anti-spooﬁng datasets for model training and do evaluation on an unseen dataset, e.g., using CASIA-MFSD [79], Oulu-NPU [80] and MSU-MFSD [81] as the sources and Idiap Replay-Attack [82] as the target.”