论文标题
多渠道注意选择gans用于指导图像到图像翻译
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
论文作者
论文摘要
我们为指导图像到图像翻译提出了一个名为多通道注意力选择生成对抗网络（Selectiongan）的新型模型，在此，我们将输入图像转换为另一个，同时尊重外部语义指导。拟议的Selectiongan明确利用了语义指导信息，并由两个阶段组成。在第一阶段，输入图像和条件语义指导被送入循环的语义引导生成网络，以产生初始的粗糙结果。在第二阶段，我们使用建议的多尺度空间合并和通道选择模块以及多通道注意选择模块来完善初始结果。此外，不确定性图会自动从注意图中学到的图形来指导像素损耗以更好地优化网络。对四个具有挑战性的指导图像到图像翻译任务（面部，手，身体和街道视图）进行详尽的实验表明，我们的选择者能够比最先进的方法产生明显更好的结果。同时，提出的框架和模块是统一的解决方案，可以应用于解决其他一代任务，例如语义图像综合。该代码可在https://github.com/ha0tang/selectiongan上找到。
We propose a novel model named Multi-Channel Attention Selection Generative Adversarial Network (SelectionGAN) for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance. The proposed SelectionGAN explicitly utilizes the semantic guidance information and consists of two stages. In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using the proposed multi-scale spatial pooling & channel selection module and the multi-channel attention selection module. Moreover, uncertainty maps automatically learned from attention maps are used to guide the pixel loss for better network optimization. Exhaustive experiments on four challenging guided image-to-image translation tasks (face, hand, body, and street view) demonstrate that our SelectionGAN is able to generate significantly better results than the state-of-the-art methods. Meanwhile, the proposed framework and modules are unified solutions and can be applied to solve other generation tasks such as semantic image synthesis. The code is available at https://github.com/Ha0Tang/SelectionGAN.
