Summary of Sohes: Self-supervised Open-world Hierarchical Entity Segmentation, by Shengcao Cao et al.
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
by Shengcao Cao, Jiuxiang Gu, Jason Kuen, Hao Tan, Ruiyi Zhang, Handong Zhao, Ani Nenkova, Liang-Yan Gui, Tong Sun, Yu-Xiong Wang
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Self-supervised Open-world Hierarchical Entity Segmentation (SOHES), a novel approach to open-world entity segmentation that eliminates the need for human annotations. SOHES operates in three phases: self-exploration, self-instruction, and self-correction. The method first produces pseudo-labels through visual feature clustering, then trains a segmentation model on these pseudo-labels, and finally rectifies noises in pseudo-labels via a teacher-student mutual-learning procedure. Beyond segmenting entities, SOHES also captures their constituent parts, providing a hierarchical understanding of visual entities. This approach achieves unprecedented performance in self-supervised open-world segmentation using raw images as the sole training data, marking a significant milestone towards high-quality open-world entity segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize objects and people in pictures without telling it what those things are. This is called “open-world entity segmentation.” Right now, this task requires lots of expert help to label the images. But researchers have come up with a new way to do this without needing any human help. They call it SOHES (Self-supervised Open-world Hierarchical Entity Segmentation). It works by creating fake labels for the images and then using those labels to teach the computer what to look for. This approach is really good at recognizing objects and people, even if they’re not things we’ve seen before. It’s a big step towards computers being able to understand pictures without needing us to tell them what’s in them. |
Keywords
» Artificial intelligence » Clustering » Self supervised
