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

Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics