  This paper proposes Attribute Attention Network (AANet), a new architecture
that integrates person attributes and attribute attention maps into a
classification framework to solve the person re-identification (re-ID) problem.
Many person re-ID models typically employ semantic cues such as body parts or
human pose to improve the re-ID performance. Attribute information, however, is
often not utilized. The proposed AANet leverages on a baseline model that uses
body parts and integrates the key attribute information in an unified learning
framework. The AANet consists of a global person ID task, a part detection task
and a crucial attribute detection task. By estimating the class responses of
individual attributes and combining them to form the attribute attention map
(AAM), a very strong discriminatory representation is constructed. The proposed
AANet outperforms the best state-of-the-art method arXiv:1711.09349v3 [cs.CV]
using ResNet-50 by 3.36% in mAP and 3.12% in Rank-1 accuracy on DukeMTMC-reID
dataset. On Market1501 dataset, AANet achieves 92.38% mAP and 95.10% Rank-1
accuracy with re-ranking, outperforming arXiv:1804.00216v1 [cs.CV], another
state of the art method using ResNet-152, by 1.42% in mAP and 0.47% in Rank-1
accuracy. In addition, AANet can perform person attribute prediction (e.g.,
gender, hair length, clothing length etc.), and localize the attributes in the
query image.
