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.

通过设计新颖的属性感知池化算法，本论文将深度卷积神经网络 (CNN) 的强大功能扩展到行人属性识别问题，充分利用不同属性之间的相关性解决多属性分类的挑战，并在基准数据集上进行了实验验证。