Improving Cross-modal Alignment for Text-Guided Image Inpainting
Yucheng Zhou, Guodong Long · 2023
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image.Existing methods are based on a strong vision encoder and a crossmodal fusion model to integrate cross-modal features.However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions.Moreover, they take cross-modal fusion for depth features, which ignores a finegrained alignment between text and image.Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have advanced in most multimodal tasks.In this work, we propose a novel model for TGII by improving cross-modal alignment (CMA).CMA model consists of a VLPM as a vision-language encoder, an image generator and global-local discriminators.To explore cross-modal alignment knowledge for image restoration, we introduce cross-modal alignment distillation and in-sample distribution distillation.In addition, we employ adversarial training to enhance the model to fill the missing region in complicated structures effectively.Experiments are conducted on two popular vision-language datasets.Results show that our model achieves state-of-the-art performance compared with other strong competitors.