  This paper expands the strength of deep convolutional neural networks (CNNs)
to the pedestrian attribute recognition problem by devising a novel attribute
aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly
applied to handle multi-attribute data because of the larger label space as
well as the attribute entanglement and correlations. We tackle these challenges
that hampers the development of CNNs for multi-attribute classification by
fully exploiting the correlation between different attributes. The multi-branch
architecture is adopted for fucusing on attributes at different regions.
Besides the prediction based on each branch itself, context information of each
branch are employed for decision as well. The attribute aware pooling is
developed to integrate both kinds of information. Therefore, attributes which
are indistinct or tangled with others can be accurately recognized by
exploiting the context information. Experiments on benchmark datasets
demonstrate that the proposed pooling method appropriately explores and
exploits the correlations between attributes for the pedestrian attribute
recognition.
