Summary of Fusing Forces: Deep-human-guided Refinement Of Segmentation Masks, by Rafael Sterzinger et al.
Fusing Forces: Deep-Human-Guided Refinement of Segmentation Masks
by Rafael Sterzinger, Christian Stippel, Robert Sablatnig
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 This paper presents an innovative approach for automating the analysis and documentation of Etruscan mirrors’ elaborate illustrations. Previous work used photometric-stereo scanning combined with deep neural networks (DNNs) to achieve expert-level annotation quality, but required human inspection and correction due to some inaccuracies. To address this limitation, the authors propose a DNN trained for interactive refinement of existing annotations based on human guidance. The human-in-the-loop approach significantly reduces manual input required while maintaining equal quality, achieving up to 75% less effort. Moreover, the methodology outperforms pure manual labeling by up to 26%, producing better results faster. This breakthrough is transferable to various applications beyond Etruscan mirrors, leveraging the model’s ability to segment intricate lines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a magic tool that can help experts analyze and document old artworks like Etruscan mirrors more efficiently. Currently, it takes a lot of time and effort to manually trace the detailed illustrations on these mirrors. The authors of this paper have developed a new way to automate this process using special computer programs called deep neural networks (DNNs). Their approach is unique because it allows humans to guide the DNN’s learning process, which results in better quality work that requires less manual input. This breakthrough has the potential to be applied to many other areas beyond Etruscan mirrors, making it a very exciting discovery. |
