Multi-Channel Attention Se-GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Paper Reading Notegit URL: https://arxiv.org/pdf/1904.06807.pdfgithub TL;DR 本文是cvpr2019的一篇关于跨视角图像翻译的一篇文章。做者提出了一种利用多通道attention模型及语义辅助GAN生成同一个场景在不一样视角下的图像，效果超过了大部分SOTA模型。 能够看到Ground Truth和SelectionGAN差
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相关文章
- 1. Image Captioning with Semantic Attention
- 2. 论文笔记：Image Captioning with Semantic Attention
- 3. Pyramid Attention Network for Semantic Segmentation
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- 5. 【翻译】Image-to-Image Translation with Conditional Adversarial Networks
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- 7. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 8. Encoder-Decoder-with-Atrous-Separable-Convolution-for-Semantic-Image-Segmentation
- 9. Guided Image-to-Image Translation with Bi-Directional Feature Transformation（ICCV19）
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