Summary of Classification Drives Geographic Bias in Street Scene Segmentation, by Rahul Nair et al.
Classification Drives Geographic Bias in Street Scene Segmentation
by Rahul Nair, Gabriel Tseng, Esther Rolf, Bhanu Tokas, Hannah Kerner
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computers and Society (cs.CY); 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 research investigates the biases in instance segmentation models trained on driving scenes from Europe. Previous studies have shown that datasets lacking geographic diversity can lead to biased performance in image recognition tasks. However, this paper examines the more complex task of instance segmentation, where models are expected to recognize and segment specific objects within images. The study finds that European-centric models are indeed geo-biased, but interestingly, this bias comes from classification errors rather than localization errors. The researchers also find that coarser classes can significantly mitigate these biases in region-specific models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computer vision models trained on driving scenes from Europe do when recognizing and segmenting specific objects like cars, buses, and trucks. Some earlier studies showed that using images from only one place or culture can make the model not work as well when it’s used somewhere else. This study takes this idea further by looking at a more complicated task called instance segmentation. They found out that models trained on European scenes are biased because they have trouble recognizing what something is (like a car) rather than knowing where it is in the picture. But, if you group similar things together, like cars and buses, the bias gets smaller. |
Keywords
» Artificial intelligence » Classification » Instance segmentation
