文本生成图像研究综述

Survey of text-to-image synthesis

对文本生成图像任务进行综合评估和整理,根据生成图像的理念,将文本生成图像任务分为3大类:基于生成对抗网络架构生成图像、基于自回归模型架构生成图像、基于扩散模型架构生成图像.针对基于生成对抗网络架构的文本生成图像方法,按照改进的不同技术点归纳为6小类:采用多层次体系嵌套架构、注意力机制的应用、应用孪生网络、采用循环一致方法、深度融合文本特征和改进无条件模型.通过对不同方法的分析,总结并讨论了现有的文本生成图像方法通用评估指标和数据集.

A comprehensive evaluation and categorization of text-to-image generation tasks were conducted.Text-to-image generation tasks were classified into three major categories based on the principles of image generation:text-to-image generation based on the generative adversarial network architecture,text-to-image generation based on the autoregressive model architecture,and text-to-image generation based on the diffusion model architecture.Improvements in different aspects were categorized into six subcategories for text-to-image generation methods based on the generative adversarial network architecture:adoption of multi-level hierarchical architectures,application of attention mechanisms,utilization of siamese networks,incorporation of cycle-consistency methods,deep fusion of text features,and enhancement of unconditional models.The general evaluation indicators and datasets of existing text-to-image methods were summarized and discussed through the analysis of different methods.