Self-attentive Semantic Segmentation Model Based On Generative Adversarial Network

Abstract

To address the problems that existing recognition networks rely on a large amount of labeled data and the perceptual field is limited to the local area of convolution, and the lack of understanding of contextual information, this paper proposes a self-attentive semantic segmentation method based on generative adversarial networks. The method is based on generating adversarial networks, constructing semantic segmentation networks and discriminators, where the semantic segmentation network uses Resnet101 as the backbone to connect the spatial pyramid pooling module of PSPNet and adopts the cross-attention method in order to overcome the problem of too many parameters of classical attention models. The model was simulated in the publicly available PASCAL VOC 2012 dataset, and the results showed that the MIoU of the model reached 73.1%, 74.4%, and 75.1% for 1/8, 1/4, and 1/2 with labels, respectively, in the first semi-supervised experiments without improving the segmentation network compared to the control group, which were 3.6%, 2.3%, and 1.3% higher, respectively. The MIoU value reached 75.4% after improving the segmentation network, which proved the superiority and effectiveness of this model.