Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification

Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex C. Kot, Gang Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5363-5372

Abstract


Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intra-sequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a de-correlation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Si_2018_CVPR,
author = {Si, Jianlou and Zhang, Honggang and Li, Chun-Guang and Kuen, Jason and Kong, Xiangfei and Kot, Alex C. and Wang, Gang},
title = {Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}