Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2210.01578v1
• Date: Tue, 4 Oct 2022 13:03:17 GMT
• Title: Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
• Authors: Yangsong Zhang, Subhankar Roy, Hongtao Lu, Elisa Ricci, St\'ephane Lathuili\`ere
• Abstract summary: We propose a self-training strategy that employs pseudo-labels to induce cooperation among multiple domain-specific classifiers. We employ feature stylization as an efficient way to generate image views that forms an integral part of self-training.
• Score: 26.79776306494929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions. To address MTDA, we propose a self-training strategy that employs pseudo-labels to induce cooperation among multiple domain-specific classifiers. We employ feature stylization as an efficient way to generate image views that forms an integral part of self-training. Additionally, to prevent the network from overfitting to noisy pseudo-labels, we devise a rectification strategy that leverages the predictions from different classifiers to estimate the quality of pseudo-labels. Our extensive experiments on numerous settings, based on four different semantic segmentation datasets, validate the effectiveness of the proposed self-training strategy and show that our method outperforms state-of-the-art MTDA approaches. Code available at: https://github.com/Mael-zys/CoaST

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