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.- Anthology ID:
- 2023.eacl-main.250
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3445–3456
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.250
- DOI:
- 10.18653/v1/2023.eacl-main.250
- Cite (ACL):
- Yucheng Zhou and Guodong Long. 2023. Improving Cross-modal Alignment for Text-Guided Image Inpainting. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3445–3456, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Improving Cross-modal Alignment for Text-Guided Image Inpainting (Zhou & Long, EACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.eacl-main.250.pdf