Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

Abstract

Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap between source domains and target domain to some extent. Then, to fully exploit the essential semantic information across source domains, we propose a collaborative learning method for domain adaptation without seeing any data from target domain. In addition, similar to the setting of unsupervised domain adaptation, unlabeled target domain data is leveraged to further improve the performance of domain adaptation. This is achieved by additionally constraining the outputs of multiple adaptation models with pseudo labels online generated by an ensembled model. Extensive experiments and ablation studies are conducted on the widely-used domain adaptation benchmark datasets in semantic segmentation. Our proposed method achieves 59.0\% mIoU on the validation set of Cityscapes by training on the labeled Synscapes and GTA5 datasets and unlabeled training set of Cityscapes. It significantly outperforms all previous state-of-the-arts single-source and multi-source unsupervised domain adaptation methods.

arXiv commentCVPR2021

References in corpus (8)

• Learning Transferable Features with Deep Adaptation Networks
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
• Unsupervised Domain Adaptation with Residual Transfer Networks
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
• Multi-source Domain Adaptation for Semantic Segmentation
• Multi-Target Domain Adaptation with Collaborative Consistency Learning
• Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
• Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization

Cited by in corpus (2)

• Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
• Semi-supervised Domain Adaptation for Semantic Segmentation