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.

通过使用多任务深度卷积神经网络 (MCNN)，共享所有属性的最低层，共享相关属性的更高层，并在 MCNN 上构建辅助网络以利用所有属性的分数来提高每个属性的最终分类，我们可以利用属性关系来提高属性分类器的准确性。我们在两个具有挑战性的公开数据集上展示了我们方法的有效性。