Person re-identification (re-ID) aims to match a specific person in a large gallery with different cameras and locations. Previous part-based methods mainly focus on part-level features with uniform partition, which increases learning ability for discriminative feature but not efficient or robust to scenarios with large variances. To address this problem, in this paper, we propose a novel feature fusion strategy based on traditional convolutional neural network. Then, a multi-branch deeper feature fusion network architecture is designed to perform discriminative learning for three semantically aligned region. Based on it, a novel self-attention mechanism is employed to softly assign corresponding weights to the semantic aligned feature during back-propagation. Comprehensive experiments have been conducted on several large-scale benchmark datasets, which demonstrates that proposed approach yields consistent and competitive re-ID accuracy compared with current single-domain re-ID methods.

中文翻译：

---

多层次的特征学习，注意重新识别人

人物重新识别（re-ID）的目的是将大型画廊中的特定人物与不同的相机和位置进行匹配。以前的基于零件的方法主要关注具有均匀划分的零件级特征，这会增加区分特征的学习能力，但对于具有较大差异的场景而言效率不高。为了解决这个问题，本文提出了一种基于传统卷积神经网络的特征融合策略。然后，设计了多分支更深层特征融合网络体系结构，以对三个语义对齐的区域执行判别式学习。在此基础上，采用一种新颖的自我注意机制在反向传播过程中将相应的权重软分配给语义对齐的特征。已经在几个大型基准数据集上进行了综合实验，