Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing

• URL: http://arxiv.org/abs/2212.03826v1
• Date: Wed, 7 Dec 2022 18:16:17 GMT
• Title: Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing
• Authors: Sarmad F. Ismael, Koray Kayabol, and Erchan Aptoula
• Abstract summary: We propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images. An image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods.
• Score: 9.118706387430883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks, have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images, that i) leads to semantically consistent and noise-free images, ii) operates with a single target domain sample (i.e. one-shot) and iii) at a fraction of the number of parameters required from state-of-the-art methods. More specifically an image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains, and a perceptual network module and loss function is further introduced to enforce semantic consistency. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods. Our source code will be available at \url{https://github.com/Sarmadfismael/LRM_I2I}.

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