  Existing evaluation metrics for Person Re-Identification (Person ReID) models
focus on system-wide performance. However, our studies reveal weaknesses due to
the uneven data distributions among cameras and different camera properties
that expose the ReID system to exploitation. In this work, we raise the
long-ignored ReID problem of camera performance imbalance and collect a
real-world privacy-aware dataset from 38 cameras to assist the study of the
imbalance issue. We propose new metrics to quantify camera performance
imbalance and further propose the Adversarial Pairwise Reverse Attention (APRA)
Module to guide the model learning the camera invariant feature with a novel
pairwise attention inversion mechanism.
