Robust Semi-Supervised Semantic Segmentation Based on Self-Attention and Spectral Normalization

The application of adversarial learning for semi-supervised semantic image segmentation based on convolutional neural networks can effectively reduce the number of manually generated labels required in the training process. However, the convolution operator of the generator in the generative adversarial network (GAN) has a local receptive field, so that the long-range dependencies between different image regions can only be modeled after passing through multiple convolutional layers. The present work addresses this issue by introducing a self-attention mechanism in the generator of the GAN to effectively account for relationships between widely separated spatial regions of the input image with supervision based on pixel-level ground truth data. In addition, the adjustment of the discriminator has been demonstrated to affect the stability of GAN training performance. This is addressed by applying spectral normalization to the GAN discriminator during the training process. The proposed stable self-attention adversarial learning semi-supervised semantic image segmentation network is demonstrated to provide superior image segmentation performance compared with the results of current semi-supervised and fully-supervised semantic image segmentation techniques.