Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
<|reference_start|>Pedestrian Attribute Recognition as Label-balanced Multi-label Learning: 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.<|reference_end|>
@article{zhou2024pedestrian,
    title={Pedestrian Attribute Recognition as Label-balanced Multi-label Learning},
    author={Yibo Zhou, Hai-Miao Hu, Yirong Xiang, Xiaokang Zhang, Haotian Wu},
    journal={arXiv preprint arXiv:2405.04858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04858},
    primaryClass={cs.CV}
}