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 this https URL

本篇论文提出了一种名为多通道注意力选择生成对抗网络（SelectionGAN）的新型模型，它将输入图像翻译成另一个图像，同时尊重外部语义指导。该模型明确利用语义指导信息，包括两个阶段：第一阶段利用周期语义引导生成网络产生初始粗略结果，第二阶段使用多尺度空间池化 & 通道选择模块和多通道注意选择模块完善初始结果。经过四项有挑战性的图像生成任务实验，证明了 SelectionGAN 能够比现有方法产生更好的结果。