MAT: Mask-Aware Transformer for Large Hole Image Inpainting (2203.15270v3)

Abstract: 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. Specifically, we customize an inpainting-oriented transformer block, where the attention module aggregates non-local information only from partial valid tokens, indicated by a dynamic mask. Extensive experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets. Code is released at https://github.com/fenglinglwb/MAT.

• The paper introduces a novel transformer-based framework that integrates convolutional layers to handle high-resolution large hole inpainting effectively.
• It employs an adjusted transformer block with feature concatenation and a dynamic mask-based attention mechanism to stabilize training and enhance GAN performance.
• Empirical results demonstrate state-of-the-art performance, with improved metrics on datasets like Places and CelebA-HQ, showcasing superior image quality and diversity.

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

The paper "MAT: Mask-Aware Transformer for Large Hole Image Inpainting" introduces a novel approach to address the problem of large hole inpainting in computer vision. The authors propose the Mask-Aware Transformer (MAT), which effectively integrates the strengths of transformers and convolutions to achieve high-fidelity, diverse image inpainting results.

Key Contributions

1. Transformer-Based Inpainting Framework: Unlike conventional methods that apply transformers at low resolutions, MAT is capable of processing high-resolution images directly. This is achieved through a thoughtfully designed architecture combining transformer blocks with convolutional layers to handle long-range dependencies effectively.
2. Adjusted Transformer Block: The authors modify the conventional transformer block by removing layer normalization and adopting feature concatenation instead of residual learning. This adjustment addresses optimization issues when dealing with large mask regions and improves the training stability and performance of the model on GAN-based tasks.
3. Efficient Attention Mechanism: The proposed Multi-Head Contextual Attention (MCA) module introduces a dynamic mask that selectively aggregates information from valid tokens, thus efficiently modeling long-range interactions while maintaining computational efficiency.
4. Style Manipulation Module: MAT includes a style manipulation module that facilitates pluralistic generation, allowing the model to produce multiple plausible inpainting solutions by modulating weights of convolutional layers based on noise inputs.
5. Empirical Validation: Extensive experiments demonstrate that MAT achieves state-of-the-art performance on benchmark datasets such as Places and CelebA-HQ, particularly in cases with large missing regions. The model's effectiveness is highlighted by improvements in metrics like FID, P-IDS, and U-IDS, indicating superior image quality and diversity.

Results and Implications

The paper reports that MAT sets new benchmarks on multiple datasets, showcasing its capability to generate photo-realistic and semantically coherent images in various contexts. Notably, MAT excels in situations where large parts of the image are missing, a challenging scenario for conventional inpainting methods.

The ability to handle high-resolution inpainting tasks without the need for pre-trained models provides an efficient and versatile solution for practical applications such as image editing, object removal, and photo restoration. Furthermore, the incorporation of style manipulation supports diverse output scenarios, enhancing the model's utility in creative and artistic domains.

Future Directions

Future developments in AI could build upon the findings of this paper, exploring even more efficient and versatile mechanisms for handling large-scale inpainting problems. Potential research avenues could include:

• Further optimizing the efficiency of attention mechanisms to reduce computational costs without compromising quality.
• Extending the model's capability to handle other challenging scenarios in inpainting, such as moving objects or dynamic backgrounds.
• Integrating semantic understanding to improve the model's contextual awareness and output coherence across diverse applications.

In conclusion, the MAT framework stands as a significant contribution to the field of image inpainting, combining innovative architectural choices with robust empirical validation to push the boundaries of what is achievable with transformers in computer vision tasks.