Global and Local Alignment Networks for Unpaired Image-to-Image Translation

• URL: http://arxiv.org/abs/2111.10346v1
• Date: Fri, 19 Nov 2021 18:01:54 GMT
• Title: Global and Local Alignment Networks for Unpaired Image-to-Image Translation
• Authors: Guanglei Yang, Hao Tang, Humphrey Shi, Mingli Ding, Nicu Sebe, Radu Timofte, Luc Van Gool, Elisa Ricci
• Abstract summary: The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style. Due to the lack of attention to the content change in existing methods, semantic information from source images suffers from degradation during translation. We introduce a novel approach, Global and Local Alignment Networks (GLA-Net) Our method effectively generates sharper and more realistic images than existing approaches.
• Score: 170.08142745705575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style while keeping unrelated contents of the input source image unchanged. However, due to the lack of attention to the content change in existing methods, the semantic information from source images suffers from degradation during translation. In the paper, to address this issue, we introduce a novel approach, Global and Local Alignment Networks (GLA-Net). The global alignment network aims to transfer the input image from the source domain to the target domain. To effectively do so, we learn the parameters (mean and standard deviation) of multivariate Gaussian distributions as style features by using an MLP-Mixer based style encoder. To transfer the style more accurately, we employ an adaptive instance normalization layer in the encoder, with the parameters of the target multivariate Gaussian distribution as input. We also adopt regularization and likelihood losses to further reduce the domain gap and produce high-quality outputs. Additionally, we introduce a local alignment network, which employs a pretrained self-supervised model to produce an attention map via a novel local alignment loss, ensuring that the translation network focuses on relevant pixels. Extensive experiments conducted on five public datasets demonstrate that our method effectively generates sharper and more realistic images than existing approaches. Our code is available at https://github.com/ygjwd12345/GLANet.

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