Multicategory Image Recognition Based on Image Semantic Features and Transformer

Abstract

In the field of computer vision research, generative adversarial networks (GAN) are used for general object recognition. In recent years, however, GAN have learned only from image data without using label information. In recent years, however, unsupervised learning, which learns GAN only from image data without using label information, where GAN are learned from image data alone without using label information, has been introduced. In this paper, we describe research on unsupervised learning of GAN since the introduction of transformer, reviewing trends in computer vision/artificial intelligence-related research since the introduction of transformer from a visual neuroscience perspective.