Robust Semantic Segmentation with Superpixel-Mix

Autor:	Franchi, Gianni, Belkhir, Nacim, Ha, Mai Lan, Hu, Yufei, Bursuc, Andrei, Blanz, Volker, Yao, Angela
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to BMVC2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.00968 Zobrazit plný text záznamu View this record from Arxiv