Reduce Information Loss in Transformers for Pluralistic Image Inpainting (2205.05076v2)

Abstract: Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer from information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration, incurring information loss and extra misalignment for the boundaries of masked regions. 2) They quantize $256^3$ RGB pixels to a small number (such as 512) of quantized pixels. The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer. Although an extra CNN network is used to upsample and refine the low-resolution results, it is difficult to retrieve the lost information back.To keep input information as much as possible, we propose a new transformer based framework "PUT". Specifically, to avoid input downsampling while maintaining the computation efficiency, we design a patch-based auto-encoder P-VQVAE, where the encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by quantization, an Un-Quantized Transformer (UQ-Transformer) is applied, which directly takes the features from P-VQVAE encoder as input without quantization and regards the quantized tokens only as prediction targets. Extensive experiments show that PUT greatly outperforms state-of-the-art methods on image fidelity, especially for large masked regions and complex large-scale datasets. Code is available at https://github.com/liuqk3/PUT

• The paper presents the PUT framework that integrates a patch-based auto-encoder (P-VQVAE) with an Un-Quantized Transformer to minimize downsampling and quantization losses.
• Experimental results on datasets like FFHQ, Places2, and ImageNet demonstrate significant improvements in image fidelity, particularly with large masked areas.
• The approach has practical implications for applications in digital art restoration and photo editing by preserving fine image details and reducing boundary artifacts.

Insights into Reducing Information Loss in Transformers for Image Inpainting

This paper addresses the challenges of pluralistic image inpainting using transformers by focusing on reducing the information loss inherent in existing methodologies. The authors identify two primary sources of information loss in current transformer-based image inpainting approaches: downsampling input images to lower resolutions and quantizing RGB pixels into discrete tokens. These steps aim to improve computational efficiency but result in significant loss of detail and inaccuracies at the boundaries of masked regions.

To tackle these issues, a novel transformer-based framework, "PUT," is introduced. The PUT framework integrates a patch-based auto-encoder known as P-VQVAE and an Un-Quantized Transformer (UQ-Transformer). This combination is designed to maximize the retention of input information while balancing computational efficiency. The P-VQVAE encoder avoids downsampling by converting masked images into non-overlapping patch tokens. The decoder maintains unmasked regions while recovering the masked areas, thus striving to minimize disturbances between regions.

The Un-Quantized Transformer further enhances this process by directly processing features from the P-VQVAE encoder without initial quantization, using quantized tokens only as prediction targets. This approach effectively bypasses the information loss associated with pixel quantization.

Strong Experimental Evidence

The framework is comprehensively tested on large-scale datasets, including FFHQ, Places2, and ImageNet, demonstrating significant improvements over state-of-the-art methods in terms of image fidelity. Notably, PUT achieves superior results, particularly in handling large masked areas and complex datasets. For instance, on ImageNet, PUT achieved a lower FID score, with a notable improvement in regions with 40-60% missing pixels.

Theoretical and Practical Implications

The implications of this research are substantial for the field of computer vision, especially in practical applications where high fidelity is crucial, such as digital art restoration, photo editing, and video content augmentation. The reduction of information loss can yield more accurate and visually pleasing inpainted images, enhancing user experience and expanding the applicability of inpainting technologies.

Future Directions

While the PUT framework presents a robust solution to the problem of information loss in pluralistic image inpainting, there are opportunities for future research. Areas of exploration could include optimizing inference speed, as current transformer-based models can be computationally intensive. Another potential direction is the investigation of alternative network architectures that may offer a better trade-off between computational overhead and output quality.

In conclusion, this paper presents a significant step forward in addressing the limitations of current transformer-based image inpainting methods by successfully reducing information loss through innovative architectural design. The proposed PUT framework not only sets a high benchmark for image fidelity but also opens up new avenues for research and application development in image inpainting and related fields.