![]() ![]() Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle consistent adversarial networks. Zili, Y., Zhang, H., Ping, T., Minglun, G.: DualGAN: unsupervised dual learning for image-to-image translation. Qifeng, C., Vladlen, K.: Photographic image synthesis with cascaded refinement networks. Sangwoo, M., Minsu, C., Jinwoo, S.: InstaGAN: instance-aware image-to-image translation. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. Leon, A.G., Alexander, S.E., Matthias, B.: Image style transfer using convolutional neural networks. In: ECCV (2016)ĭeepak, P., Philipp, K., Jeff, D., Trevor, D., Alexei, A.E.: Context encoders: feature learning by inpainting. Richard, Z., Phillip, I., Alexei, A.E.: Colorful image colorization. Ma, T., Tian, W.: Back-projection-based progressive growing generative adversarial network for single image super-resolution. Mikaeli, E., Aghagolzadeh, A., Azghani, M.: Single-image super-resolution via patch-based and group-based local smoothness modeling. Ĭhao, D., Chen, C.L., Kaiming, H., Xiaoou, T.: Learning a deep convolutional network for image super-resolution. The source code and trained models are available at. We also show that the computation complexity of the proposed module is linear to the image size moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. ![]() In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. ![]() To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. ![]()
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