  Attributes, or semantic features, have gained popularity in the past few
years in domains ranging from activity recognition in video to face
verification. Improving the accuracy of attribute classifiers is an important
first step in any application which uses these attributes. In most works to
date, attributes have been considered to be independent. However, we know this
not to be the case. Many attributes are very strongly related, such as heavy
makeup and wearing lipstick. We propose to take advantage of attribute
relationships in three ways: by using a multi-task deep convolutional neural
network (MCNN) sharing the lowest layers amongst all attributes, sharing the
higher layers for related attributes, and by building an auxiliary network on
top of the MCNN which utilizes the scores from all attributes to improve the
final classification of each attribute. We demonstrate the effectiveness of our
method by producing results on two challenging publicly available datasets.
