DTT-NET: Dual-Domain Translation Transformer For Semi-Supervised Image Deraining

We propose a novel assessment approach to the performance of image manipulation using natural language descriptions in this paper. Text-guided image manipulation aims to modify an input image aligned with the text description and has recently become a hot topic for its usefulness. For the assessment, we focus on the similarity between ?the change in text features? and ?the change in image features? and define the direction of the changes as Manipulation Direction (MD). By capturing the changes in each different modality, MD can take into account the direction of image manipulation and quantify the extent to which the image is manipulated aligned with the text description. To the best of our knowledge, this is the first metric specialized in text-guided image manipulation. Experimental results show that MD can calculate evaluation scores that are correlated with subjective scores toward the manipulated images.