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The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsuperviseddomain adaptive model in semantic segmentation tasks for industrial wastesorting. In this paper, we introduce the SIA_Adapt method, which incorporatesseveral methods for domain adaptive models. The core of our method in thetransferable representation from large-scale pre-training. In this process, wechoose a network architecture that differs from the state-of-the-art for domainadaptation. After that, self-training using pseudo-labels helps to make theinitial adaptation model more adaptable to the target domain. Finally, themodel soup scheme helped to improve the generalization performance in thetarget domain. Our method SIA_Adapt achieves 1st place in the VisDA2022challenge. The code is available on https://github.com/DaehanKim-Korea/VisDA2022_Winner_Solution.

1st Place Solution to NeurIPS 2022 Challenge on Visual Domain Adaptation