aiqbench analysis
This study reveals that geographic bias in street scene instance segmentation stems primarily from classification errors rather than localization failures when models are trained on region-specific data like European driving scenes. The findings suggest a practical mitigation strategy where coarser object classes can significantly reduce these biases without compromising the robustness of spatial detection. For developers deploying autonomous driving systems globally, this highlights the critical need for diverse training datasets and careful class granularity to ensure equitable performance across different regions.
Classification Drives Geographic Bias in Street Scene Segmentation
arXiv:2412.11061v2 Announce Type: replace-cross 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).
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).
Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
arXiv:2412.11061 [cs.CV]
(or arXiv:2412.11061v2 [cs.CV] for this version)
R. Nair, B. Tokas, G. Tseng, E. Rolf and H. Kerner, "Classification Drives Geographic Bias in Street Scene Segmentation," 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
https://doi.org/10.1109/CVPRW67362.2025.00068 DOI(s) linking to related resources
Full text under the publisher's open licence · 207 words
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