Part-level representations are important for robust person re-identification (ReID), but in practice feature quality suffers due to the body part misalignment problem. In this paper, we present a robust, compact, and easy-to-use method called the Multi-task Part-aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images. MPN solves the body part misalignment problem via multi-task learning (MTL) in the training stage. More specifically, it builds one main task (MT) and one auxiliary task (AT) for each body part on the top of the same backbone model. The ATs are equipped with a coarse prior of the body part locations for training images. ATs then transfer the concept of the body parts to the MTs via optimizing the MT parameters to identify part-relevant channels from the backbone model. Concept transfer is accomplished by means of two novel alignment strategies: namely, parameter space alignment via hard parameter sharing and feature space alignment in a class-wise manner. With the aid of the learned high-quality parameters, MTs can independently extract semantically aligned part-level features from relevant channels in the testing stage. MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain. Systematic experiments on four large-scale ReID databases demonstrate that MPN consistently outperforms state-of-the-art approaches by significant margins.

中文翻译：

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具有粗略先验的多任务学习用于稳健的部分感知人员重新识别

部分级表示对于稳健的人员重新识别 (ReID) 很重要，但在实践中，由于身体部分未对齐问题，特征质量会受到影响。在本文中，我们提出了一种强大、紧凑且易于使用的方法，称为多任务部分感知网络 (MPN)，旨在从行人图像中提取语义对齐的部分级特征。MPN 在训练阶段通过多任务学习 (MTL) 解决身体部位错位问题。更具体地说，它在同一主干模型的顶部为每个身体部位构建一个主要任务（MT）和一个辅助任务（AT）。AT 配备了用于训练图像的身体部位位置的粗略先验。然后，AT 通过优化 MT 参数将身体部位的概念传递给 MT，以从骨干模型中识别与部位相关的通道。概念转移是通过两种新颖的对齐策略完成的：即通过硬参数共享的参数空间对齐和以类别方式的特征空间对齐。借助学习到的高质量参数，MT 可以在测试阶段从相关通道中独立提取语义对齐的部分级特征。MPN 具有三个关键优势：1）在推理阶段不需要进行身体部位检测；2）它的模型对于训练和测试都非常紧凑和高效；3）在训练阶段，它只需要身体部位位置的粗略先验，这很容易获得。对四个大型 ReID 数据库的系统实验表明，MPN 始终以显着的优势优于最先进的方法。