Improving Cross-modal Alignment for Text-Guided Image Inpainting

• URL: http://arxiv.org/abs/2301.11362v1
• Date: Thu, 26 Jan 2023 19:18:27 GMT
• Title: Improving Cross-modal Alignment for Text-Guided Image Inpainting
• Authors: Yucheng Zhou, Guodong Long
• Abstract summary: Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. We propose a novel model for TGII by improving cross-modal alignment. Our model achieves state-of-the-art performance compared with other strong competitors.
• Score: 36.1319565907582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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