A Co-Attention Method Based on Generative Adversarial Networks for Multi-view Images

Abstract

In this paper, we use Deep Convolutional Generative Adversarial Networks (DCGANs) method to generate more images with multiple views to increase our dataset diversity. We use 3D-model different views for training DCGAN to make interpolation between the leftest and rightest random vectors, which means it can generate leftest to rightest images. After producing many of multi-view images, we combine with CNN based modules called co-attention map generator to look for common features of the same class but in different views clothing. By applying the learned generator to all images, the corresponding co-attention maps are obtained. we can fluently apply the proposed method can function well for multi-view objects on different types of clothing classes.