论文标题
本地和全球gan具有语义了解图像生成的语义提升采样
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
论文作者
论文摘要
在本文中，我们解决了语义引导图像生成的任务。大多数现有图像级生成方法常见的一个挑战是难以生成小物体和详细的本地纹理。为了解决这个问题，在这项工作中，我们考虑使用本地上下文生成图像。因此，我们使用语义图作为指导设计了一个本地类别的生成网络，该网络分别构建和学习不同类别的子生成器，使其能够捕获更细节的细节。为了了解本地一代的更多歧视性类别特异性特征表示，我们还提出了一个新颖的分类模块。为了结合全球图像级和特定于本地班级特定的一代的优势，联合生成网络的设计具有注意力融合模块和嵌入的双歧节结构。最后，我们提出了一种新颖的语义意识上采样方法，该方法具有更大的接收场，并且可以采用遥远的像素，这些像素与功能上的提升相关，从而使其能够更好地保留具有相同语义标签的实例的语义一致性。对两个图像生成任务进行的广泛实验表明了该方法的出色性能。最先进的结果是在任务和九个具有挑战性的公共基准上建立的。源代码和训练有素的模型可在https://github.com/ha0tang/lggan上找到。
In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To address this, in this work we consider generating images using local context. As such, we design a local class-specific generative network using semantic maps as guidance, which separately constructs and learns subgenerators for different classes, enabling it to capture finer details. To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module. To combine the advantages of both global image-level and local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Lastly, we propose a novel semantic-aware upsampling method, which has a larger receptive field and can take far-away pixels that are semantically related for feature upsampling, enabling it to better preserve semantic consistency for instances with the same semantic labels. Extensive experiments on two image generation tasks show the superior performance of the proposed method. State-of-the-art results are established by large margins on both tasks and on nine challenging public benchmarks. The source code and trained models are available at https://github.com/Ha0Tang/LGGAN.
