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的一篇關於跨視角圖像翻譯的一篇文章。做者提出了一種利用多通道attention模型及語義輔助GAN生成同一個場景在不一樣視角下的圖像，效果超過了大部分SOTA模型。 能夠看到Ground Truth和SelectionGAN差
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相關文章
- 1. Image Captioning with Semantic Attention
- 2. 論文筆記：Image Captioning with Semantic Attention
- 3. Pyramid Attention Network for Semantic Segmentation
- 4. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 5. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 6. Unsupervised Attention-guided Image-to-Image Translation 解讀
- 7. [論文筆記] 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）
- 10. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯