Adversarial Semi-Supervised Semantic Segmentation with Attention Mechanism

Image semantic segmentation is one of the most important research topics in computer vision. The current semantic segmentation algorithm based on full convolutional neural network has some problems, such as lack of correlation between pixels, convolution kernel receptive field smaller than the theoretical value, and high label cost of manually labeled data set. In order to solve the above problems, an antithesis semi-supervised semantic segmentation model integrating attention mechanism is proposed. The generative adversarial network is applied to image semantic segmentation to enhance the correlation between pixels. In this model, self-attention module and multi-core pooling module are added to generate network to fuse long distance semantic information, and the convolution kernel receptive field is enlarged. A large number of experiments are carried out on PASCAL VOC2012 enhanced dataset and Cityscapes dataset, and the experimental results prove the validity and reliability of the proposed method for image semantic segmentation. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.