MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Understand

Recent studies have shown the importance of modeling long-range interactions in the inpainting problem.

• To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost.
• In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images.
• We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images.