Pedestrian Attribute Recognition as Label-balanced Multi-label Learning

• URL: http://arxiv.org/abs/2405.04858v1
• Date: Wed, 8 May 2024 07:27:15 GMT
• Title: Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
• Authors: Yibo Zhou, Hai-Miao Hu, Yirong Xiang, Xiaokang Zhang, Haotian Wu,
• Abstract summary: We propose a novel framework that successfully decouples label-balanced data re-sampling from the curse of attributes co-occurrence. Our work achieves best accuracy on various popular benchmarks, and importantly, with minimal computational budget.
• Score: 12.605514698358165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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