Summary of Remove: a Reference-free Metric For Object Erasure, by Aditya Chandrasekar et al.
ReMOVE: A Reference-free Metric for Object Erasure
by Aditya Chandrasekar, Goirik Chakrabarty, Jai Bardhan, Ramya Hebbalaguppe, Prathosh AP
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper introduces ReMOVE, a novel metric for assessing object erasure efficacy in diffusion-based image editing models. Unlike existing measures like LPIPS and CLIPScore, ReMOVE addresses the challenge of evaluating inpainting without a reference image, which is common in practical scenarios. The new metric effectively distinguishes between object removal and replacement, aligning with the intuitive definition of inpainting. ReMOVE correlates with state-of-the-art metrics and human perception, providing a finer-grained evaluation of generated outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how good an image editing model is at removing objects from pictures. The old way of doing this didn’t work well when you don’t have a picture of what the object looked like before it was removed. This new method, called ReMOVE, is better because it can tell if the model did a good job of just removing the object or also changing other parts of the picture. It works by looking at how similar the edited picture is to an original picture that doesn’t have the object in it. |
Keywords
» Artificial intelligence » Diffusion
