Multi-part compact bilinear CNN for person re-identification

In paper, we present a novel multi-part compact bilinear convolutional neural network (CNN) model, which consists of a bilinear CNN and two part-networks aiming to learn the global features and the finer local features simultaneously. The bilinear operation is simplified with recently proposed compact bilinear pooling method, and bilinear vectors are averagely pooled to keep more local spatial information. The proposed model is trained by using a histogram loss function in order to reduce the distribution overlap of positive pairs and negative pairs. Experiments show that, the combination of compact bilinear CNN and histogram loss can significantly improve the original models, and performs favorably compared to the state of the art.