Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning-based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced content and style are is crucial in evaluating the quality of stylized images. In this paper, we propose an end-to-end two-stream fully convolutional networks (FCNs) aiming at balancing the contributions of the content and the style in rendered images. Our proposed network consists of the encoder and decoder parts. The encoder part utilizes a FCN for content and a FCN for style where the two FCNs have feature injections and are independently trained to preserve the semantic content and to learn the faithful style representation in each. The semantic content feature and the style representation feature are then concatenated adaptively and fed into the decoder to generate style-transferred (stylized) images. In order to train our proposed network, we employ a loss network, the pre-trained VGG-16, to compute content loss and style loss, both of which are efficiently used for the feature injection as well as the feature concatenation. Our intensive experiments show that our proposed model generates more balanced stylized images in content and style than state-of-the-art methods. Moreover, our proposed network achieves efficiency in speed.

中文翻译：

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两流FCN平衡内容和样式以进行样式转移

样式转移是按照给定样式渲染给定图像内容，它在计算机视觉基础研究和工业应用中都具有重要作用。随着基于深度学习的方法的成功使用，最近又重新推出了该问题，但由于在保留内容和忠实呈现样式之间进行了权衡，因此仍然是一项艰巨的任务。实际上，在评估风格化图像的质量时，如何平衡内容和样式至关重要。在本文中，我们提出了一种端到端两流全卷积网络（FCN），旨在平衡渲染图像中内容和样式的贡献。我们建议的网络由编码器和解码器部分组成。编码器部分将FCN用于内容，将FCN用于样式，其中两个FCN具有特征注入，并经过独立训练以保留语义内容并学习每个样式中的忠实样式表示。然后，将语义内容特征和样式表示特征进行自适应连接，并将其输入到解码器中，以生成样式转换（风格化）的图像。为了训练我们提出的网络，我们使用了损失网络（经过预先训练的VGG-16）来计算内容损失和样式损失，这两种方法都可有效地用于特征注入和特征级联。我们的密集实验表明，与最新方法相比，我们提出的模型在内容和样式上生成更加平衡的风格化图像。此外，我们提出的网络可提高速度效率。