Reliable Semantic Segmentation with Superpixel-Mix

• URL: http://arxiv.org/abs/2108.00968v1
• Date: Mon, 2 Aug 2021 15:13:52 GMT
• Title: Reliable Semantic Segmentation with Superpixel-Mix
• Authors: Gianni Franchi, Nacim Belkhir, Mai Lan Ha, Yufei Hu, Andrei Bursuc, Volker Blanz, Angela Yao
• Abstract summary: We introduce Superpixel-mix, a new superpixel-based data augmentation method with teacher-student consistency training. Our proposed technique achieves state-of-the-art results in semi-supervised semantic segmentation on the Cityscapes dataset.
• Score: 25.288512209672326
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertainty and reduced bias. To improve reliability, we introduce Superpixel-mix, a new superpixel-based data augmentation method with teacher-student consistency training. Unlike other mixing-based augmentation techniques, mixing superpixels between images is aware of object boundaries, while yielding consistent gains in segmentation accuracy. Our proposed technique achieves state-of-the-art results in semi-supervised semantic segmentation on the Cityscapes dataset. Moreover, Superpixel-mix improves the reliability of semantic segmentation by reducing network uncertainty and bias, as confirmed by competitive results under strong distributions shift (adverse weather, image corruptions) and when facing out-of-distribution data.

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