Re-Identification With Consistent Attentive Siamese Networks

Meng Zheng, Srikrishna Karanam, Ziyan Wu, Richard J. Radke; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5735-5744

Abstract


We propose a new deep architecture for person re-identification (re-id). While re-id has seen much recent progress, spatial localization and view-invariant representation learning for robust cross-view matching remain key, unsolved problems. We address these questions by means of a new attention-driven Siamese learning architecture, called the Consistent Attentive Siamese Network. Our key innovations compared to existing, competing methods include (a) a flexible framework design that produces attention with only identity labels as supervision, (b) explicit mechanisms to enforce attention consistency among images of the same person, and (c) a new Siamese framework that integrates attention and attention consistency, producing principled supervisory signals as well as the first mechanism that can explain the reasoning behind the Siamese framework's predictions. We conduct extensive evaluations on the CUHK03-NP, DukeMTMC-ReID, and Market-1501 datasets and report competitive performance.

Related Material


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[bibtex]
@InProceedings{Zheng_2019_CVPR,
author = {Zheng, Meng and Karanam, Srikrishna and Wu, Ziyan and Radke, Richard J.},
title = {Re-Identification With Consistent Attentive Siamese Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}