Recently, language-guided global image editing draws increasing attention
with growing application potentials. However, previous GAN-based methods are
not only confined to domain-specific, low-resolution data but also lacking in
interpretability. To overcome the collective difficulties, we develop a
text-to-operation model to map the vague editing language request into a series
of editing operations, e.g., change contrast, brightness, and saturation. Each
operation is interpretable and differentiable. Furthermore, the only
supervision in the task is the target image, which is insufficient for a stable
training of sequential decisions. Hence, we propose a novel operation planning
algorithm to generate possible editing sequences from the target image as
pseudo ground truth. Comparison experiments on the newly collected MA5k-Req
dataset and GIER dataset show the advantages of our methods. Code is available
at this https URL

该研究提出了一种基于文本到操作的模型，用于将编辑语言请求转换为一系列可解释且可区分的编辑操作，同时提出了一种操作规划算法以生成从目标图像中的伪地面真实性中可能的编辑序列。