  Recognising detailed facial or clothing attributes in images of people is a
challenging task for computer vision, especially when the training data are
both in very large scale and extremely imbalanced among different attribute
classes. To address this problem, we formulate a novel scheme for batch
incremental hard sample mining of minority attribute classes from imbalanced
large scale training data. We develop an end-to-end deep learning framework
capable of avoiding the dominant effect of majority classes by discovering
sparsely sampled boundaries of minority classes. This is made possible by
introducing a Class Rectification Loss (CRL) regularising algorithm. We
demonstrate the advantages and scalability of CRL over existing
state-of-the-art attribute recognition and imbalanced data learning models on
two large scale imbalanced benchmark datasets, the CelebA facial attribute
dataset and the X-Domain clothing attribute dataset.
