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.

翻译：过去十年来,随着视觉识别的深度学习的快速发展,个人重新身份(ReID)在过去十年中取得了显著的进展,同时,在视觉识别的快速学习中,深入的学习模式中通常持有的i.d.(独立和完全分布的)假设,对于ReID来说有些不适用,因为REID的目标是在不同地点,在不同地点,在照相机中,发现同一行人同同同行人的照片,而不同地点的照相机,这些地点的相像往往具有可变和独立的域特性,这些特性也受视偏差数据分布的影响。在这项工作中,我们建议采用一种特征分布差异-分布差异和校准(PECA)方法,为个人再识别(个人再ID)提供通用特征表示,这种通用不仅在摄像机之间同时进行歧视,而且具有敏感性和可部署到和可部署到任意不见的目标目标区域。具体地,我们进行永久性地地地特征分配的扰动,避免模型过分适应每个来源(见)的域段分布(见)域特性,对调调调幅的特性和调换;此外,我们设计一个全球校准机制机制,使所有源域域域域域的地差分配,通过取消原则原则,改进原分配。