Classification Drives Geographic Bias in Street Scene Segmentation

• URL: http://arxiv.org/abs/2412.11061v1
• Date: Sun, 15 Dec 2024 05:33:10 GMT
• Title: Classification Drives Geographic Bias in Street Scene Segmentation
• Authors: Rahul Nair, Gabriel Tseng, Esther Rolf, Bhanu Tokas, Hannah Kerner,
• Abstract summary: We investigated geo-biases in real-world driving datasets on a more complex task: instance segmentation. We found that geo-biases came from classification errors rather than localization errors. Our findings show that in region-specific models, geo-biases can be significantly mitigated by using coarser classes.
• Score: 20.14340857253721
• License:
• Abstract: Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image datasets (e.g., ImageNet) and simple tasks like image recognition, we investigated geo-biases in real-world driving datasets on a more complex task: instance segmentation. We examined if instance segmentation models trained on European driving scenes (Eurocentric models) are geo-biased. Consistent with previous work, we found that Eurocentric models were geo-biased. Interestingly, we found that geo-biases came from classification errors rather than localization errors, with classification errors alone contributing 10-90% of the geo-biases in segmentation and 19-88% of the geo-biases in detection. This showed that while classification is geo-biased, localization (including detection and segmentation) is geographically robust. Our findings show that in region-specific models (e.g., Eurocentric models), geo-biases from classification errors can be significantly mitigated by using coarser classes (e.g., grouping car, bus, and truck as 4-wheeler).

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