demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches.

Our result (c) Application: Edit object appearance (b) Application: Change label types (a) Synthesized resultFigure 1: We propose a generative adversarial framework for synthesizing 2048 × 1024 images from semantic label maps (lower left corner in (a)). Compared to previous work [5], our results express more natural textures and details. (b) We can change labels in the original label map to create new scenes, like replacing trees with buildings. (c) Our framework also allows a user to edit the appearance of individual objects in the scene, e.g. changing the color of a car or the texture of a road. Please visit our website for more side-by-side comparisons as well as interactive editing demos.

强大的模拟器高度降低了在培训和评估自动车辆时对真实测试的需求。数据驱动的模拟器蓬勃发展，最近有条件生成对冲网络（CGANS）的进步，提供高保真图像。主要挑战是在施加约束之后的同时合成光量造型图像。在这项工作中，我们建议通过重新思考鉴别者架构来提高所生成的图像的质量。重点是在给定对语义输入生成图像的问题类上，例如场景分段图或人体姿势。我们建立成功的CGAN模型，提出了一种新的语义感知鉴别器，更好地指导发电机。我们的目标是学习一个共享的潜在表示，编码足够的信息，共同进行语义分割，内容重建以及粗糙的粒度的对抗性推理。实现的改进是通用的，并且可以应用于任何条件图像合成的任何架构。我们展示了我们在场景，建筑和人类综合任务上的方法，跨越三个不同的数据集。代码可在https://github.com/vita-epfl/semdisc上获得。

提供和渲染室内场景一直是室内设计的一项长期任务，艺术家为空间创建概念设计，建立3D模型的空间，装饰，然后执行渲染。尽管任务很重要，但它很乏味，需要巨大的努力。在本文中，我们引入了一个特定领域的室内场景图像合成的新问题，即神经场景装饰。鉴于一张空的室内空间的照片以及用户确定的布局列表，我们旨在合成具有所需的家具和装饰的相同空间的新图像。神经场景装饰可用于以简单而有效的方式创建概念室内设计。我们解决这个研究问题的尝试是一种新颖的场景生成体系结构，它将空的场景和对象布局转化为现实的场景照片。我们通过将其与有条件图像合成基线进行比较，以定性和定量的方式将其进行比较，证明了我们提出的方法的性能。我们进行广泛的实验，以进一步验证我们生成的场景的合理性和美学。我们的实现可在\ url {https://github.com/hkust-vgd/neural_scene_decoration}获得。

我们提出了一种用于自动驾驶应用的图像增强的组成方法。它是一个端到端的神经网络，被训练，以便无缝地构成作为从物体图像的裁剪补片所代表的物体（例如，车辆或行人）进入背景场景图像。由于我们的方法强调了组合图像的语义和结构一致性，而不是它们的像素级RGB精度，我们通过结构感知功能来定制我们网络的输入和输出，相应地设计了我们的网络损耗。具体而言，我们的网络从输入场景图像中获取语义布局特征，从输入对象补丁中的边缘和剪影编码的功能，以及潜像作为输入的潜在代码，并生成定义平移和缩放的2D空间仿射变换对象补丁。学习的参数进一步进入可分扩展的空间变压器网络，以将对象补丁转换为目标图像，其中我们的模型通过仿射变换鉴别器和布局鉴别器对其进行对面的培训。我们评估我们的网络，为结构感知组成，在质量，可组合性和复合图像的概念方面，在突出的自动驾驶数据集上。对最先进的替代品进行比较，确认我们的方法的优越性。

Labels to Facade BW to Color Aerial to Map Labels to Street Scene Edges to Photo input output input input input input output output output output input output Day to Night Figure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output image.These problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to pixels. Conditional adversarial nets are a general-purpose solution that appears to work well on a wide variety of these problems. Here we show results of the method on several. In each case we use the same architecture and objective, and simply train on different data.

与生成对抗网络（GAN）的图像和分割掩模的联合合成有望减少用像素通过像素注释收集图像数据所需的精力。但是，要学习高保真图像掩码合成，现有的GAN方法首先需要一个需要大量图像数据的预训练阶段，这限制了其在受限图像域中的利用。在这项工作中，我们迈出了一步，以减少此限制，从而引入了单次图像掩码合成的任务。我们旨在仅给出一个单个标记的示例，生成各种图像及其分割面具，并假设与以前的模型相反，则无法访问任何预训练数据。为此，我们受到单图像gan的最新体系结构发展的启发，我们介绍了OSMIS模型，该模型可以合成分割掩模，这些掩模与单次镜头中生成的图像完全一致。除了实现产生的口罩的高保真度外，OSMIS在图像合成质量和多样性中的最先进的单图像模型优于最先进的单位图。此外，尽管没有使用任何其他数据，OSMIS还是表现出令人印象深刻的能力，可以作为一击细分应用程序的有用数据增强的来源，提供了与标准数据增强技术相辅相成的性能提高。代码可从https://github.com/ boschresearch/One-shot-synthesis获得

驾驶模拟器在开发和测试新的智能车辆系统中起着重要作用。模拟的视觉保真度对于构建基于视觉的算法和进行人体驱动器实验至关重要。低视觉保真度破坏了对人类驾驶实验的沉浸。传统的计算机图形管道使用详细的3D型号，网格，纹理和渲染引擎来生成3D场景的2D图像。这些过程是劳动密集型的，并且不会产生逼真的图像。在这里，我们介绍了一种混合生成神经图形管道，以改善驾驶模拟的视觉保真度。给定一个3D场景，我们仅部分渲染了感兴趣的重要对象，例如车辆，并使用生成的对抗过程来综合背景和其余图像。为此，我们提出了一种新颖的图像形成策略，以从3D风景中形成2D语义图像，由简单对象模型组成而无需纹理。然后将这些语义图像转换为具有现实世界驾驶场景中训练的最先进的生成对抗网络（GAN），将其转换为逼真的RGB图像。这用随机生成但逼真的表面取代重复性。最后，将部分渲染和GAN合成的图像与混合gan混合。我们表明，使用该方法生成的图像的光真相更为类似于现实世界中的驾驶数据集，例如CityScapes和Kitti，而不是传统方法。这种比较是使用语义保留分析和特雷切特的距离（FID）测量进行的。

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quan-titative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region. Code: https://github.com/ZPdesu/SEAN .

培训监督图像综合模型需要批评评论权来比较两个图像：结果的原始真相。然而，这种基本功能仍然是一个公开问题。流行的方法使用L1（平均绝对误差）丢失，或者在预先预留的深网络的像素或特征空间中。然而，我们观察到这些损失倾向于产生过度模糊和灰色的图像，以及其他技术，如GAN需要用于对抗这些伪影。在这项工作中，我们介绍了一种基于信息理论的方法来测量两个图像之间的相似性。我们认为，良好的重建应该具有较高的相互信息与地面真相。这种观点使得能够以对比的方式学习轻量级批评者以“校准”特征空间，使得相应的空间贴片的重建被置于擦除其他贴片。我们表明，当用作L1损耗的替代时，我们的配方立即提升输出图像的感知现实主义，有或没有额外的GaN丢失。

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively. We demonstrate state-of-theart performance in semantic segmentation on two challenging "synthetic-2-real" set-ups 1 and show that the approach can also be used for detection.

生成的对抗网络（GANS）产生高质量的图像，但致力于训练。它们需要仔细正常化，大量计算和昂贵的超参数扫描。我们通过将生成和真实样本投影到固定的预级特征空间中，在这些问题上进行了重要的头路。发现鉴别者无法充分利用来自预押模型的更深层次的特征，我们提出了更有效的策略，可以在渠道和分辨率中混合特征。我们预计的GaN提高了图像质量，样品效率和收敛速度。它与最多一个百万像素的分辨率进一步兼容，并在二十二个基准数据集上推进最先进的FR \'Echet Inception距离（FID）。重要的是，预计的GAN符合先前最低的FID速度快40倍，鉴于相同的计算资源，将壁钟时间从5天切割到不到3小时。

To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feedforward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required any longer at generation time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.

我们介绍了与给定单个图像的任意长相机轨迹相对应的长期视图的新面积视图的问题。这是一个具有挑战性的问题，远远超出了当前视图合成方法的能力，这在提出大型摄像机运动时快速退化。用于视频生成的方法也具有有限的生产长序列的能力，并且通常不适用于场景几何形状。我们采用混合方法，它以迭代`\ emph {render}，\ emph {refine}，\ emph {重复}'框架集成了几何和图像合成，允许在数百帧之后覆盖大距离的远程生成。我们的方法可以从一组单目的视频序列训练。我们提出了一个沿海场景的空中镜头数据集，并比较了我们最近的观看综合和有条件的视频生成基线的方法，表明它可以在与现有方法相比，在大型相机轨迹上产生更长的时间范围。项目页面https://infinite-nature.github.io/。

我们介绍了GANFormer2模型，这是一个迭代对象的变压器，探讨了生成建模的任务。该网络包含强大明显的结构前导者，以反映视觉场景的组成性质，并通过连续过程合成图像。它以两个阶段运行：快速轻薄的规划阶段，在其中我们起草高级场景布局，然后采用基于关注的执行阶段，其中布局正在改进，进化为丰富和详细的图片。我们的模型远离传统的黑盒GAN架构，该架构具有平坦和单片潜在的空间，朝向透明设计，鼓励效率，可控性和可解释性。我们通过仔细评估在一系列数据集中仔细评估来展示Ganformer2的优势和素质，从多对象CLEVR场景到挑战的Coco图像，显示它在视觉质量，多样性和一致性方面成功实现了最先进的性能。进一步的实验表明了模型的解剖学，并提供了更深入的洞察力进入其生成过程，因为它从粗略的初始草图逐步进行，详细的布局，用于考虑对象的深度和依赖项，最终达到最终的布局决心描绘充满活力和复杂的现实世界场景。有关模型实现，请参阅https://github.com/dorarad/gansformer。