论文标题
pi-trans：平行-CONVMLP和基于隐式转换的GAN，用于跨视图图像翻译
PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation
论文作者
论文摘要
对于语义引导的跨视图图像翻译，至关重要的是要了解从源视图图像中采样像素的地方以及在目标视图语义映射的指导下重新分配它们的位置，尤其是在源图像和目标图像之间几乎没有重叠或急剧的视图差异时。因此，不仅需要在源查看图像和目标视图语义映射中编码像素之间的长距离依赖关系，而且还需要翻译这些学到的依赖关系。为此，我们提出了一个新颖的生成对抗网络Pi-Trans，该网络主要由一个新型的平行-CONVMLP模块和一个在多个语义级别上的隐式转换模块组成。广泛的实验结果表明，与两个具有挑战性的数据集中的最新方法相比，Pi-Trans可以通过很大的边距实现最佳的定性和定量性能。源代码可在https://github.com/amazingren/pi-trans上找到。
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images. Hence, one not only needs to encode the long-range dependencies among pixels in both the source view image and target view semantic map but also needs to translate these learned dependencies. To this end, we propose a novel generative adversarial network, PI-Trans, which mainly consists of a novel Parallel-ConvMLP module and an Implicit Transformation module at multiple semantic levels. Extensive experimental results show that PI-Trans achieves the best qualitative and quantitative performance by a large margin compared to the state-of-the-art methods on two challenging datasets. The source code is available at https://github.com/Amazingren/PI-Trans.
