Summary of Assessing Image Inpainting Via Re-inpainting Self-consistency Evaluation, by Tianyi Chen et al.
Assessing Image Inpainting via Re-Inpainting Self-Consistency Evaluation
by Tianyi Chen, Jianfu Zhang, Yan Hong, Yiyi Zhang, Liqing Zhang
First submitted to arxiv on: 25 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new approach to evaluating image inpainting models that ensures consistency and fidelity in the absence of reference images. The traditional methods rely on comparing the inpainted image to the original, but this favors certain outcomes and introduces biases. Instead, the authors propose a self-supervised metric based on multiple re-inpainting passes, which emphasizes self-consistency and reduces biases. This method is validated through extensive experiments across various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image inpainting is a technique that fills in missing parts of an image using information from other parts of the same image or from other images. Sometimes, this process can be tricky because it’s hard to make sure the filled-in part looks right. Right now, we evaluate how well an image inpainting algorithm works by comparing it to the original image, but this method is flawed because it favors certain outcomes and doesn’t give a fair score to all possible solutions. To solve this problem, researchers have come up with a new way to measure how good an image inpainting algorithm is. This approach looks at how well the algorithm does when it’s asked to fill in different parts of the same image multiple times. This helps to reduce the bias and give a more accurate score. |
Keywords
» Artificial intelligence » Image inpainting » Self supervised
