  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 cross-modal 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 fine-grained 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.
