Summary of Human-in-the-loop Segmentation Of Multi-species Coral Imagery, by Scarlett Raine et al.
Human-in-the-Loop Segmentation of Multi-species Coral Imagery
by Scarlett Raine, Ross Marchant, Brano Kusy, Frederic Maire, Niko Suenderhauf, Tobias Fischer
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Robotics (cs.RO)
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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 Recent advances in large foundation models have facilitated the creation of augmented ground truth masks using only features extracted by the denoised version of the DINOv2 foundation model and K-Nearest Neighbors (KNN), without any pre-training. Point label propagation is a technique that uses existing images labeled with sparse points to create augmented ground truth data, which can be used to train a semantic segmentation model. This work shows that using denoised DINOv2 features with KNN improves on the prior state-of-the-art by 2.7% for pixel accuracy and 5.8% for mIoU (5 grid points). Additionally, the proposed human-in-the-loop labeling method outperforms the prior state-of-the-art by 14.2% for pixel accuracy and 19.7% for mIoU when there are 5 point labels per image. The paper also presents a comprehensive study on the impacts of point label placement style and the number of points on the point label propagation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many pictures of coral reefs, but labeling them is very time-consuming. This paper shows how to use computers to help with this task. They use special computer models called “foundation models” that can learn from small amounts of labeled data and create more labels automatically. This helps scientists who study coral reefs by reducing the amount of work they need to do to prepare their images for analysis. The results are quite good, and this method could be useful in many other fields where labeling images is a big task. |
Keywords
» Artificial intelligence » Semantic segmentation
