| In real life,the forms of information are diverse.Effective fusion and interaction between information play a key role in fields such as computer vision and natural language processing.This is also one of the reasons why multimodal research has received widespread attention in recent years.Text-guided image generation,as a hot topic in this field,not only provides a more intuitive representation for text recognition tasks,but also brings more flexible means for image generation research.Its application range covers virtual reality,animation,art-aided design,and other fields.The text-based image generation method aims to transform the semantic information in natural language into images,so as to achieve more accurate and effective image generation.With the development of deep learning technology,the generative adversarial networks(GAN)has become one of the most popular methods in the field of image generation.Its basic idea is to train the generator and the discriminator against each other,which is to let the discriminator judge the authenticity of the image generated by the generator,in order to make the image generated more realistic.However,in the current image generation technology,due to the lack of accurate modeling of the relationship between text and image,many methods have some problems,such as insufficient diversity of generated images,lack of image details,etc.To solve these problems,based on existing methods,this paper proposes a dual-channel consistency constraint generative adversarial network(CC-GAN).The innovative work of this article is as follows:(1)This paper proposes a dual-channel consistency constraint generative adversarial network,(CC-GAN)in which dual-channel refers to the sentence-level text and word-level text.Consistency constraint refers to the similarity judgment of a given text and a generated image,which constrains the two to achieve consistency.Use the basic framework of GAN to generate high-quality images,and accurately model the relationship between text and image through the dual-channel consistency constraint module,so as to generate images more in line with the semantic requirements of the text.(2)Use two channels to process the text at different scales,recognize and analyze the information in the text through sentence level and word level,in order to achieve accurate recognition of text semantics,and introduce text information into the generator.The dualchannel processing method can greatly improve the accuracy and efficiency of the algorithm,effectively solve some bottlenecks of traditional text processing algorithms,and ensure full recognition of given text semantics.(3)This paper proposes a kind of consistency loss of image and text matching.The consistency constraint module accurately models the relationship between text and image,and monitors the generator to generate images that are more in line with the text requirements together with the generator loss and condition enhancement loss.Through the comprehensive function of these loss functions,the generator can ensure the authenticity and diversity of the generated image,and further improve the consistency and matching between the image and the text,which has an important application prospect in practical operation.In terms of the experiment,this paper uses two datasets,CUB200-2011 dataset and the MS COCO dataset.The experimental results show that compared with other text-based image generation methods,CC-GAN has greatly improved the quality and diversity of the generated images.At the same time,this method can also accurately identify the semantic information of the text,ensuring the consistency between the generated image and the given text.In general,CC-GAN,a two-channel consistent constraint generation adversarial network framework,provides an effective idea and method for the further development of text-based image generation technology.This method has good practicability and can play an important role in various practical application scenarios. |
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