  Multi-label recognition is a fundamental, and yet is a challenging task in
computer vision. Recently, deep learning models have achieved great progress
towards learning discriminative features from input images. However,
conventional approaches are unable to model the inter-class discrepancies among
features in multi-label images, since they are designed to work for image-level
feature discrimination. In this paper, we propose a unified deep network to
learn discriminative features for the multi-label task. Given a multi-label
image, the proposed method first disentangles features corresponding to
different classes. Then, it discriminates between these classes via increasing
the inter-class distance while decreasing the intra-class differences in the
output space. By regularizing the whole network with the proposed loss, the
performance of applying the wellknown ResNet-101 is improved significantly.
Extensive experiments have been performed on COCO-2014, VOC2007 and VOC2012
datasets, which demonstrate that the proposed method outperforms
state-of-the-art approaches by a significant margin of 3:5% on large-scale COCO
dataset. Moreover, analysis of the discriminative feature learning approach
shows that it can be plugged into various types of multi-label methods as a
general module.
