计算机工程与应用2024，Vol.60Issue(17)：224-232,9.DOI:10.3778/j.issn.1002-8331.2312-0186
基于注意力和动态记忆模块的文本图像生成方法
Text-to-Image Generation Method Based on Attention and Dynamic Memory Module
摘要
Abstract
Aiming at the problems existing in multi-stage generative models in the text generation image task,such as the lack of image texture information features and the poor consistency between text descriptions and generated images,this paper proposes a novel generative adversarial network(ADM-GAN)model.The model is optimized using attention and dynamic memory modules.In the initial stage,the text description is converted into embedding vectors through a text encoder,and a generator is used to combine random noise to generate low-resolution images.Then,the paper introduces spatial attention and channel attention modules,aiming to fuse low-resolution image hidden features with important word-level semantic features,thereby ensuring the consistency of text description and image features.Finally,the dynamic memory module is used to capture the semantic correspondence between text and images,and dynamically adjust the memory content according to the generation process,refine the image texture,and improve the text-to-image synthesis effect.Through comparative experiments on the public CUB and COCO data sets,compared with previous methods,the Fréchet inception distance and inception score of this paper have been significantly improved,proving that this model can solve the problem of lack of image details and semantic information to a certain extent.It effectively improves the consis-tency between images and text,and achieves better results.关键词
文本生成图像/生成对抗网络/注意力机制/动态记忆Key words
text-to-image/generative adversarial network/attention mechanism network/dynamic memory分类
信息技术与安全科学引用本文复制引用
张鹤,雷浩鹏,王明文,张尚昆..基于注意力和动态记忆模块的文本图像生成方法[J].计算机工程与应用,2024,60(17):224-232,9.基金项目
江西省自然科学基金面上项目(20224BAB202018) （20224BAB202018）
国家自然科学基金(62266023). （62266023）
