Heterogeneous Feature Re-Sampling for Balanced Pedestrian Attribute Recognition

In pedestrian attribute recognition (PAR), the loose umbrella term 'attribute' ranges from human soft-biometrics to wearing accessory, and even extending to various subjective body descriptors. As a result, the vast coverage of 'attributes' implies that, instead of being over-specialized to limited attributes with exclusive characteristic, PAR should be approached from a much fundamental perspective. To this end, given that most attributes are greatly under-represented in real-world datasets, we simply distill PAR into a visual task of multi-label recognition under significant data imbalance. Accordingly, we introduce feature re-sampled detached learning (FRDL) to decouple label-balanced learning from the curse of attributes co-occurrence. Specifically, FRDL is able to balance the sampling distribution of an attribute without biasing the label prior of co-occurring others. As a complementary method, we also propose gradient-oriented augment translating (GOAT) to alleviate the feature noise and semantics imbalance aggravated in FRDL. Integrated in a highly unified framework, FRDL and GOAT substantially refresh the state-of-the-art performance on various realistic benchmarks, while maintaining a minimal computational budget. Further analytical discussion and experimental evidence corroborate the veracity of our advancement: this is the first work that establishes labels-independent and impartial balanced learning for PAR.