Summary of Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters, by Eden Grad et al.
Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters
by Eden Grad, Moshe Kimhi, Lion Halika, Chaim Baskin
First submitted to arxiv on: 16 Jun 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 paper investigates the challenges in instance segmentation, a complex task that requires accurate labeling of object boundaries within images. To evaluate the robustness of instance segmentation models, the authors introduce two new datasets: COCO-N and Cityscapes-N, simulating different levels of annotation noise. Additionally, they propose a benchmark for weakly annotated noisy labels, dubbed COCO-WAN, which utilizes foundation models and weak annotations to simulate semi-automated annotation tools. The study highlights the quality of segmentation masks produced by various models and challenges popular methods designed to address learning with label noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make sure instance segmentation models are accurate. This is hard because it requires a lot of information about each object in an image, including where it is and what it is. The authors create new datasets that show different levels of mistakes in labeling these objects. They also come up with a way to test how well the models work when they get wrong information. This study helps us understand how good our models are at finding things in pictures. |
Keywords
* Artificial intelligence * Instance segmentation
