论文标题
多域无监督图像到图像翻译，外观自适应卷积
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution
论文作者
论文摘要
在过去的几年中，已经提出了图像到图像（I2i）翻译方法将给定的图像转换为各种输出。尽管取得了令人印象深刻的结果，但它们主要集中于两个域之间的I2i翻译，因此多域I2i翻译仍然是一个挑战。为了解决这个问题，我们提出了一种新型的多域无监督图像到图像翻译（MDUIT）框架，该框架利用分解的内容特征和外观自适应卷积将图像转换为目标外观，同时保留给定的几何内容。我们还利用了一个对比度学习目标，该目标提高了分离能力，并通过配对语义上相似的图像来有效地利用训练过程中的多域图像数据。这使我们的方法仅使用一个框架学习了多个视觉域之间的不同映射。我们表明，与最先进的方法相比，所提出的方法在多个领域产生了视觉上的多样性和合理的结果。
Over the past few years, image-to-image (I2I) translation methods have been proposed to translate a given image into diverse outputs. Despite the impressive results, they mainly focus on the I2I translation between two domains, so the multi-domain I2I translation still remains a challenge. To address this problem, we propose a novel multi-domain unsupervised image-to-image translation (MDUIT) framework that leverages the decomposed content feature and appearance adaptive convolution to translate an image into a target appearance while preserving the given geometric content. We also exploit a contrast learning objective, which improves the disentanglement ability and effectively utilizes multi-domain image data in the training process by pairing the semantically similar images. This allows our method to learn the diverse mappings between multiple visual domains with only a single framework. We show that the proposed method produces visually diverse and plausible results in multiple domains compared to the state-of-the-art methods.
