  Most deep learning methods that achieve high segmentation accuracy require
deep network architectures that are too heavy and complex to run on embedded
devices with limited storage and memory space. To address this issue, this
paper proposes an efficient Generative Adversarial Transfomer (GATrans) for
achieving high-precision semantic segmentation while maintaining an extremely
efficient size. The framework utilizes a Global Transformer Network (GTNet) as
the generator, efficiently extracting multi-level features through residual
connections. GTNet employs global transformer blocks with progressively linear
computational complexity to reassign global features based on a learnable
similarity function. To focus on object-level and pixel-level information, the
GATrans optimizes the objective function by combining structural similarity
losses. We validate the effectiveness of our approach through extensive
experiments on the Vaihingen dataset, achieving an average F1 score of 90.17%
and an overall accuracy of 91.92%.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing