  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.
