Learning Prior Feature and Attention Enhanced Image Inpainting (2208.01837v2)

Abstract: Many recent inpainting works have achieved impressive results by leveraging Deep Neural Networks (DNNs) to model various prior information for image restoration. Unfortunately, the performance of these methods is largely limited by the representation ability of vanilla Convolutional Neural Networks (CNNs) backbones.On the other hand, Vision Transformers (ViT) with self-supervised pre-training have shown great potential for many visual recognition and object detection tasks. A natural question is whether the inpainting task can be greatly benefited from the ViT backbone? However, it is nontrivial to directly replace the new backbones in inpainting networks, as the inpainting is an inverse problem fundamentally different from the recognition tasks. To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process. Moreover, we propose to use attention priors from MAE to make the inpainting model learn more long-distance dependencies between masked and unmasked regions. Sufficient ablations have been discussed about the inpainting and the self-supervised pre-training models in this paper. Besides, experiments on both Places2 and FFHQ demonstrate the effectiveness of our proposed model. Codes and pre-trained models are released in https://github.com/ewrfcas/MAE-FAR.

• The paper introduces the Feature and Attention enhanced Restoration (FAR) architecture, leveraging Vision Transformers and Masked AutoEncoder pre-training to provide rich feature and attention priors for improved image inpainting.
• Experiments on datasets like Places2 and FFHQ show the FAR model significantly outperforms existing methods across various metrics, including PSNR, SSIM, and FID, producing more coherent and detailed results.
• Integrating MAE into inpainting demonstrates the potential to adapt recognition-focused architectures for generative tasks, opening possibilities for broader applications in medical imaging, satellite imagery, and other restoration domains.

Learning Prior Feature and Attention Enhanced Image Inpainting

This paper introduces a novel approach to image inpainting, leveraging Vision Transformers (ViTs) coupled with Masked AutoEncoder (MAE) pre-training to enhance the process. The proposed architecture, dubbed Feature and Attention enhanced Restoration (FAR), capitalizes on the strength of ViTs to model informative priors, thereby overcoming limitations inherent in conventional Convolutional Neural Networks (CNNs).

Overview of Methodology

The paper focuses on incorporating ViT backbone models, specifically pre-trained MAEs, into the image inpainting pipeline. The general strategy involves:

1. Masked AutoEncoder (MAE) Integration: The MAE, pre-trained with a high masking ratio, is employed in the inpainting model to provide rich informative priors. The MAE's pre-training facilitates understanding of long-range dependencies and better modeling of global features, crucial for high-quality inpainting results.
2. Attention Priors: Beyond feature integration, attention maps from the MAE are utilized. The authors propose using attention weights among masked and unmasked regions to inform the inpainting process, improving the ability of the model to reason over long-distance dependencies in images.
3. High-Resolution (HR) Inpainting: The paper addresses challenges associated with HR image inpainting by proposing effective methods to extend their approach to higher resolutions. This includes the use of positional encodings that are continuous and adapted for HR images.
4. Training and Finetuning: A comprehensive training regime is utilized, involving dynamic resizing during finetuning. The use of layer-specific learnable scalars within FAR allows for granular tuning of feature integration across different encoder layers.

Experimental Results

The proposed methods were tested on datasets such as Places2 and FFHQ, demonstrating substantial improvements over existing state-of-the-art models. Key findings include:

• Quantitative Improvements: Across various metrics such as PSNR, SSIM, FID, and LPIPS, the proposed model consistently outperformed baselines like LaMa and Co-Mod. A distinct improvement was noted for models applying MAE-derived attention priors.
• Qualitative Insights: Visual comparisons further emphasize the effectiveness of the proposed method in generating coherent structures and texture details in masked regions. The method showed superior capability, particularly at HR settings, where accurate semantic understanding is crucial.

Practical and Theoretical Implications

The work presents significant implications both practically and theoretically:

• Advancements in Inverse Problem Solving: The integration of MAE into inpainting tasks exemplifies how architectures primarily used for recognition tasks can be successfully adapted for generative implementations.
• Potential for Broader Applications: The methodologies proposed can extend to various domains requiring image restoration, including medical imaging, satellite imagery, and more, broadening the scope of transfer learning in computational vision.

Future Directions

Building upon this paper, future work could explore:

• Extension to Diverse Data Domains: Applying this technique to datasets beyond visual should be considered, such as 3D modeling or video data, where contextual inpainting can vastly improve model applicability.
• Layer-wise Feature Utilization: Further exploration into the utility of features from different transformer layers could yield insights into optimal feature extraction strategies.
• Cross-Dataset Pre-training: Given the success of pre-training on large datasets, investigation into the effects of using even larger or more diverse datasets during MAE pre-training could enhance generalization further.

In conclusion, this paper provides a robust framework for utilizing ViTs and MAE in image inpainting, setting a precedent for future research in leveraging deep learning models for advanced image restoration tasks. The detailed explorations ensure high-quality inpainting even in challenging conditions, marking a significant contribution to the field of computer vision.