  Rooting in the scarcity of most attributes, realistic pedestrian attribute
datasets exhibit unduly skewed data distribution, from which two types of model
failures are delivered: (1) label imbalance: model predictions lean greatly
towards the side of majority labels; (2) semantics imbalance: model is easily
overfitted on the under-represented attributes due to their insufficient
semantic diversity. To render perfect label balancing, we propose a novel
framework that successfully decouples label-balanced data re-sampling from the
curse of attributes co-occurrence, i.e., we equalize the sampling prior of an
attribute while not biasing that of the co-occurred others. To diversify the
attributes semantics and mitigate the feature noise, we propose a Bayesian
feature augmentation method to introduce true in-distribution novelty. Handling
both imbalances jointly, our work achieves best accuracy on various popular
benchmarks, and importantly, with minimal computational budget.
