Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.

翻译：近年来，行人属性识别研究通过显式或隐式建模属性间的共现关系取得了进展。考虑到这种先验知识在具体场景中具有高度可变性和不可预测性，我们证明现有方法实际上难以将这种拟合的属性依赖关系泛化到数据集分布之外的场景或身份上，从而导致属性共现的潜在偏差。为使模型在实际场景中具有鲁棒性，我们提出属性解耦特征学习，确保对某一属性的识别不会推断其他属性是否存在，并依次将其形式化为互信息最小化问题。基于此，我们设计了高效解耦属性的实用策略，显著提升了基线性能，并在PETAzs和RAPzs等真实数据集上取得了最先进的结果。代码已发布在https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition。