  Pedestrian attribute recognition is an important multi-label classification
problem. Although the convolutional neural networks are prominent in learning
discriminative features from images, the data imbalance in multi-label setting
for fine-grained tasks remains an open problem. In this paper, we propose a new
re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly
enhance the ability to discriminate the fewer attributes via increasing the
proportion of labels accounting for a small part. Fundamentally, by applying
over-sampling and under-sampling on the multi-label dataset at the same time,
the thought of robbing the rich attributes and helping the poor makes a
significant contribution to DAI. Extensive empirical evidence shows that our
DAI algorithm achieves state-of-the-art results, based on pedestrian attribute
datasets, i.e. standard PA-100K and PETA datasets.
