WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels
Shengjie Liu, Chuang Zhu, Yuan Li, Wenqi Tang
Abstract
Unsupervised domain adaptation (UDA) for semantic segmentation addresses the cross-domain problem with fine source domain labels. However, the acquisition of semantic labels is often time-consuming, many scenarios only have weak labels (e.g. bounding boxes). When weak supervision and cross-domain problems coexist, this paper defines a new task: unsupervised domain adaptation based on weak source domain labels (WUDA). To explore solutions for WUDA, this paper proposes two intuitive frameworks and conducts comparative experiments. We observe that the two frameworks behave differently when the datasets change. Therefore, we construct datasets with a wide range of domain shifts and conduct extended experiments to analyze the impact of domain shift changes on the two frameworks. In addition, to measure domain shift, we apply the metric representation shift to urban landscape image segmentation for the first time. The source code and constructed datasets can be obtained from this link: https://github.com/bupt-ai-cz/WUDA.
BibTeX
@inproceedings{icassp2023_wudaunsupervised,
title = {WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels},
author = {Shengjie Liu and Chuang Zhu and Yuan Li and Wenqi Tang},
booktitle = {ICASSP 2023},
year = {2023}
}