Abstract: In order to effectively improve the visual effect and matching degree of text matching generated images, a text matching generated image model based on improved GAN( Generating Adversarial Networks) algorithm is proposed. Initial matching of text and images are unfolded through a mixed index tree. On the basis of GAN, they are improved to form an adversarial generation network based on cross attention mechanism encoding, and the improved GAN is used to establish a text matching image generation model. The cross attention encoder in the bidirectional LSTM( Long Short-Term Memory) network optimization model is used to translate and align text and visual information, obtaining cross modal mapping relationships between text and images, completing fine matching between text and images, and ultimately generating images that meet the requirements of the text. The experimental results show that the proposed model can generate images with higher quality that match image details with text.

Key words: improved generating adversarial networks(GAN) algorithm, text matching, image generation model