Abstract
Recent generative adversarial network (GAN) based methods (e.g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation. Some frameworks have been proposed to adopt a segmentation network as the auxiliary regularization to prevent the content distortion. However, all of them require extra pixel-wise annotations, which is difficult to fulfill in practical applications. In this paper, we propose a novel GAN (namely OP-GAN) to address the problem, which involves a self-supervised module to enforce the image content consistency during image-to-image translations without any extra annotations. We evaluate the proposed OP-GAN on three publicly available datasets. The experimental results demonstrate that our OP-GAN can yield visually plausible translated images and significantly improve the semantic segmentation accuracy in different domain adaptation scenarios with off-the-shelf deep learning networks such as PSPNet and U-Net.
X. Xie and J. Chen—The first two authors are equal contribution.
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Notes
- 1.
The analysis of grid size can be found in Appendix.
- 2.
The network architecture of the shared-weight encoder is presented in Appendix.
- 3.
After excluding the domain specific information, the content features \(\tilde{p}\) from different domains are directly comparable. So, we use the simple mean squared error to measure the difference.
- 4.
The detailed training process with self-supervised loss can be found in Appendix.
- 5.
The top-1 solution (without extra training data) on the leaderboard of semantic segmentation on CamVid: https://paperswithcode.com/sota/semantic-segmentation-on-camvid.
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Acknowledge
This work is supported by the Natural Science Foundation of China (No. 91959108 and 61702339), the Key Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), National Key Research and Development Project (2018YFC2000702) and Science and Technology Program of Shenzhen, China (No. ZDSYS201802021814180).
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Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., Zheng, Y. (2020). Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_30
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