Exploiting interaction of fine and coarse features and attribute co-occurrence for person attribute recognition

Person attribute recognition, i.e., the prediction of a fixed set of semantic attributes given an image of a person, becomes an important topic in the field of computer vision. Recently, methods based on convolutional neural networks have shown outstanding performance in this area. They usually employ a CNN network to mine the shared feature representation followed by several layers for attribute classification. To improve the representation ability of the model, many methods element-add or concatenate coarse and fine feature maps to fuse information at different feature levels. However, these methods didn’t fully exploit the interaction of multi-level convolutional feature maps for person attribute analysis and not consider the correlation of attributes for the same person. In this paper, we introduce a kind of correlation feature, which exploits the high order interaction of coarse and fine feature maps to capture the robust feature representation from multi-level convolution layers as the image representation for person attribute recognition. Moreover, we propose an intraperson attribute loss to explicitly model the correlation of attributes for the same person. We experiment our proposed model on CIFAR-10 dataset, Berkeley Human Attributes dataset, PA-100 K dataset, and experimental results show the better performance of the feature representation and the effectiveness of intra-person attribute loss.