Person re-identification aims to identify the same pedestrian across
non-overlapping camera views. Deep learning techniques have been applied for
person re-identification recently, towards learning representation of
pedestrian appearance. This paper presents a novel Contextual-Attentional
Attribute-Appearance Network (CA3Net) for person re-identification. The CA3Net
simultaneously exploits the complementarity between semantic attributes and
visual appearance, the semantic context among attributes, visual attention on
attributes as well as spatial dependencies among body parts, leading to
discriminative and robust pedestrian representation. Specifically, an attribute
network within CA3Net is designed with an Attention-LSTM module. It
concentrates the network on latent image regions related to each attribute as
well as exploits the semantic context among attributes by a LSTM module. An
appearance network is developed to learn appearance features from the full
body, horizontal and vertical body parts of pedestrians with spatial
dependencies among body parts. The CA3Net jointly learns the attribute and
appearance features in a multi-task learning manner, generating comprehensive
representation of pedestrians. Extensive experiments on two challenging
benchmarks, i.e., Market-1501 and DukeMTMC-reID datasets, have demonstrated the
effectiveness of the proposed approach.

本文介绍了一种新的人物再识别方法 CA3Net，它利用深度学习技术，同时利用语义属性和视觉外观，以及身体部位之间的空间依赖关系，学习生成具有鉴别性和鲁棒性的行人表示。实验证明，该方法在 Market-1501 和 DukeMTMC-reID 数据集上取得了很好的效果。