Summary of Rclicks: Realistic Click Simulation For Benchmarking Interactive Segmentation, by Anton Antonov et al.
RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation
by Anton Antonov, Andrey Moskalenko, Denis Shepelev, Alexander Krapukhin, Konstantin Soshin, Anton Konushin, Vlad Shakhuro
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 tackles the issue of interactive segmentation in image editing tasks, where users directly influence their output through prompts. Current methods rely on assumptions about click patterns, but recent studies show this may not always be the case. To improve performance, the authors conducted a large crowdsourcing study to collect 475K real-user clicks and developed a clickability model that simulates actual user inputs. They propose RClicks benchmark for evaluating interactive segmentation methods’ average quality and robustness w.r.t. click patterns. The results show that existing models may perform worse than reported in baseline benchmarks, highlighting the need for more realistic evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can help people edit pictures by allowing them to correct mistakes. Right now, computer programs make big assumptions about how people will interact with them when editing pictures. But new research shows that these assumptions are not always true. To fix this, the authors asked a lot of people to try editing pictures and recorded what they did. They then used this data to create a special tool that can predict how people will edit pictures in the future. This allows them to test if different computer programs are good at helping people edit pictures or not. The results show that many existing programs may not be as good as we thought, which is important information for making better programs. |
