Summary of Active Learning with Context Sampling and One-vs-rest Entropy For Semantic Segmentation, by Fei Wu et al.
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation
by Fei Wu, Pablo Marquez-Neila, Hedyeh Rafi-Tarii, Raphael Sznitman
First submitted to arxiv on: 9 Dec 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 proposed OREAL method is a novel patch-based Active Learning (AL) approach designed for multi-class semantic segmentation tasks in computer vision. Existing AL methods often overlook boundary pixels, which are crucial for accurate segmentation. The OREAL method addresses this limitation by employing maximum aggregation of pixel-wise uncertainty scores to enhance boundary detection. Additionally, it introduces one-vs-rest entropy, a novel uncertainty score function that computes class-wise uncertainties while achieving implicit class balancing during dataset creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OREAL is an innovative way to make computer vision tasks easier and faster. Right now, making datasets for these tasks takes a lot of time and effort. Active Learning helps by choosing which data points to annotate first. But most methods don’t take into account the important information at the boundaries between different classes. OREAL fixes this problem by using uncertainty scores to highlight the most useful boundary pixels. This makes the segmentation task more accurate. |
Keywords
» Artificial intelligence » Active learning » Semantic segmentation
