  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.
