Person Re-identification (ReID) has been advanced remarkably over the last 10 years along with the rapid development of deep learning for visual recognition. However, the i.i.d. (independent and identically distributed) assumption commonly held in most deep learning models is somewhat non-applicable to ReID considering its objective to identify images of the same pedestrian across cameras at different locations often of variable and independent domain characteristics that are also subject to view-biased data distribution. In this work, we propose a Feature-Distribution Perturbation and Calibration (PECA) method to derive generic feature representations for person ReID, which is not only discriminative across cameras but also agnostic and deployable to arbitrary unseen target domains. Specifically, we perform per-domain feature-distribution perturbation to refrain the model from overfitting to the domain-biased distribution of each source (seen) domain by enforcing feature invariance to distribution shifts caused by perturbation. Furthermore, we design a global calibration mechanism to align feature distributions across all the source domains to improve the model generalization capacity by eliminating domain bias. These local perturbation and global calibration are conducted simultaneously, which share the same principle to avoid models overfitting by regularization respectively on the perturbed and the original distributions. Extensive experiments were conducted on eight person ReID datasets and the proposed PECA model outperformed the state-of-the-art competitors by significant margins.

本论文提出了一种名为PECA的方法，通过特征分布扰动和校准的方式来获得可部署于任意未知目标域的人员重新识别的通用特征表示，这是一个既具有相机间区分性，而对任意未知目标域又具有不可知性。该方法可消除因领域倾向性而导致的模型过度拟合，通过局部扰动和全局校准的方式实现不同领域之间的特征分布对齐来提高模型的泛化能力，在8个人员重新识别数据集上，提出的PECA模型明显优于现有的对手。

特征分布扰动与校准用于广义个体重识别