Improving Cross-modal Alignment for Text-Guided Image Inpainting

TL;DR

This paper introduces a novel text-guided image inpainting model that leverages vision-language pre-trained models and cross-modal alignment techniques to improve the quality of restored images, achieving state-of-the-art results.

Contribution

The work proposes a new CMA model that enhances cross-modal alignment in TGII using distillation and adversarial training, addressing previous computational and alignment limitations.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively restores complex missing regions guided by text.

Improves cross-modal alignment through distillation techniques.

Abstract

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.