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

arXiv 2211.14596 ↗First posted 26 Nov 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting. In this paper, we introduce the SIA_Adapt method, which incorporates several methods for domain adaptive models. The core of our method in the transferable representation from large-scale pre-training. In this process, we choose a network architecture that differs from the state-of-the-art for domain adaptation. After that, self-training using pseudo-labels helps to make the initial adaptation model more adaptable to the target domain. Finally, the model soup scheme helped to improve the generalization performance in the target domain. Our method SIA_Adapt achieves 1st place in the VisDA2022 challenge. The code is available on https: //github.com/DaehanKim-Korea/VisDA2022_Winner_Solution.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 32 references that are in the map. First posted on arXiv 26 Nov 2022.

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References · 28

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16 more
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