DUDA: Online-Offline Dual Domain Adaption for Semantic Segmentation

Antao Pan (Zhejiang University),* Yawei Luo (Zhejiang University), Yi Yang (Zhejiang University), Jun Xiao (Zhejiang University)
The 33rd British Machine Vision Conference


Self-training-based methods have achieved superior performance on unsupervised domain adaptive semantic segmentation task. However, these methods severely suffer from noisy pseudo label assignment. In this paper, we propose a simple yet effective dual pseudo label updating method that employs both online and offline mechanisms to dynamically update the two groups of pseudo labels. The online updating module employs a mean model to generate pseudo labels on-the-fly while the offline updating module capitalizes on the temporal consistency information to correct noisy labels. Furthermore, we present an online-offline dual regularization to further improve the noise-tolerant ability of the model. Combining the online-offline dual updating and online-offline dual regularization, we propose a novel mean-teacher based framework dubbed \textit{Online-Offline Dual Domain Adaption} (DUDA). Experiments show the proposed DUDA brings large performance gain and achieves state-of-the-art performance on two challenging benchmarks: GTA-to-Cityscapes and SYNTHIA-to-Cityscapes ($58.4\%$ mIoU and $59.7\%$ mIoU respectively).



author    = {Antao Pan and Yawei Luo and Yi Yang and Jun Xiao},
title     = {DUDA: Online-Offline Dual Domain Adaption for Semantic Segmentation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {https://bmvc2022.mpi-inf.mpg.de/0585.pdf}

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