Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation

• URL: http://arxiv.org/abs/2003.00867v2
• Date: Mon, 30 Mar 2020 06:56:11 GMT
• Title: Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
• Authors: Myeongjin Kim, Hyeran Byun
• Abstract summary: It is challenging for a model trained with synthetic data to generalize to real data. We diversity the texture of synthetic images using a style transfer algorithm. We fine-tune the model with self-training to get direct supervision of the target texture.
• Score: 19.617821473205694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic domain and real domain, it is challenging for a model trained with synthetic data to generalize to real data. In this paper, considering the fundamental difference between the two domains as the texture, we propose a method to adapt to the texture of the target domain. First, we diversity the texture of synthetic images using a style transfer algorithm. The various textures of generated images prevent a segmentation model from overfitting to one specific (synthetic) texture. Then, we fine-tune the model with self-training to get direct supervision of the target texture. Our results achieve state-of-the-art performance and we analyze the properties of the model trained on the stylized dataset with extensive experiments.

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