The performance of person re-identification (Re-ID) has been seriously
effected by the large cross-view appearance variations caused by mutual
occlusions and background clutters. Hence learning a feature representation
that can adaptively emphasize the foreground persons becomes very critical to
solve the person Re-ID problem. In this paper, we propose a simple yet
effective foreground attentive neural network (FANN) to learn a discriminative
feature representation for person Re-ID, which can adaptively enhance the
positive side of foreground and weaken the negative side of background.
Specifically, a novel foreground attentive subnetwork is designed to drive the
network's attention, in which a decoder network is used to reconstruct the
binary mask by using a novel local regression loss function, and an encoder
network is regularized by the decoder network to focus its attention on the
foreground persons. The resulting feature maps of encoder network are further
fed into the body part subnetwork and feature fusion subnetwork to learn
discriminative features. Besides, a novel symmetric triplet loss function is
introduced to supervise feature learning, in which the intra-class distance is
minimized and the inter-class distance is maximized in each triplet unit,
simultaneously. Training our FANN in a multi-task learning framework, a
discriminative feature representation can be learned to find out the matched
reference to each probe among various candidates in the gallery. Extensive
experimental results on several public benchmark datasets are evaluated, which
have shown clear improvements of our method over the state-of-the-art
approaches.

本研究提出了一种简单而有效的前景注意力神经网络（FANN），通过引入前景针对子网和特征融合子网学习判别性特征表示来解决人员再识别问题，在多任务学习框架中训练 FANN，可以学习到一种判别性特征表示，能够在各种候选项中寻找与每个探针匹配的参考值。与最先进的方法相比，该方法在多个公共基准数据集上都显示出明显的改进。