Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis 1 .

Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement the state-ofthe-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects 1 .

Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff 1 , which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things.

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach nearhuman semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems.

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

The Mapillary Vistas Dataset is a novel, largescale street-level image dataset containing 25 000 highresolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5× larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-theart methods for visual road-scene understanding.

TU Dresden www.cityscapes-dataset.net train/val -fine annotation -3475 images train -coarse annotation -20 000 images test -fine annotation -1525 images

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-regionbased context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixellevel prediction. The proposed approach achieves state-ofthe-art performance on various datasets. It came first in Im-ageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.

The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very timeconsuming to obtain; image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9% mIOU over image-level supervision. Further, we demonstrate that models trained with pointlevel supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget.

Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes 1 .

我们介绍了遮阳板，一个新的像素注释的新数据集和一个基准套件，用于在以自我为中心的视频中分割手和活动对象。遮阳板注释Epic-kitchens的视频，其中带有当前视频分割数据集中未遇到的新挑战。具体而言，我们需要确保像素级注释作为对象经历变革性相互作用的短期和长期一致性，例如洋葱被剥皮，切成丁和煮熟 - 我们旨在获得果皮，洋葱块，斩波板，刀，锅以及表演手的准确像素级注释。遮阳板引入了一条注释管道，以零件为ai驱动，以进行可伸缩性和质量。总共，我们公开发布257个对象类的272K手册语义面具，990万个插值密集口罩，67K手动关系，涵盖36小时的179个未修剪视频。除了注释外，我们还引入了视频对象细分，互动理解和长期推理方面的三个挑战。有关数据，代码和排行榜：http：//epic-kitchens.github.io/visor

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-

Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced 'el-vis'): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ∼2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org.

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.

视频分析的图像分割在不同的研究领域起着重要作用，例如智能城市，医疗保健，计算机视觉和地球科学以及遥感应用。在这方面，最近致力于发展新的细分策略;最新的杰出成就之一是Panoptic细分。后者是由语义和实例分割的融合引起的。明确地，目前正在研究Panoptic细分，以帮助获得更多对视频监控，人群计数，自主驾驶，医学图像分析的图像场景的更细致的知识，以及一般对场景更深入的了解。为此，我们介绍了本文的首次全面审查现有的Panoptic分段方法，以获得作者的知识。因此，基于所采用的算法，应用场景和主要目标的性质，执行现有的Panoptic技术的明确定义分类。此外，讨论了使用伪标签注释新数据集的Panoptic分割。继续前进，进行消融研究，以了解不同观点的Panoptic方法。此外，讨论了适合于Panoptic分割的评估度量，并提供了现有解决方案性能的比较，以告知最先进的并识别其局限性和优势。最后，目前对主题技术面临的挑战和吸引不久的将来吸引相当兴趣的未来趋势，可以成为即将到来的研究研究的起点。提供代码的文件可用于：https：//github.com/elharroussomar/awesome-panoptic-egation

Waterbodies和附近相关对象的基于视觉的语义分割提供了管理水资源和处理洪水紧急情况的重要信息。然而，缺乏用于水相关类别的大规模标记培训和测试数据集可防止研究人员在计算机视野中研究水有关的问题。为了解决这个问题，我们呈现亚特兰蒂斯，一个新的水平和相关对象的语义分割的新基准。亚特兰蒂斯由5,195张Waterbodies图像组成，以及56级物体的高质量像素级手动注释，其中包括17级人为物体，18级自然对象和21个一般课程。我们详细介绍了亚特兰蒂斯，并在我们的基准上评估了几种最先进的语义分段网络。此外，通过在两个不同的路径中加工水生和非水生植物来制定新的深度神经网络水平，用于水体语义分割。 Aquanet还包含低级功能调制和交叉路径调制，可增强特征表示。实验结果表明，拟议的Aquanet优于亚特兰蒂斯的其他最先进的语义细分网络。我们声称，亚特兰蒂斯是最大的水体图像数据集，用于语义分割，提供各种水和水有关的类，它将有利于计算机视觉和水资源工程的研究人员。

本文介绍了Omnicity，这是一种从多层次和多视图图像中了解无所不能的城市理解的新数据集。更确切地说，Omnicity包含多视图的卫星图像以及街道级全景图和单视图图像，构成了超过100k像素的注释图像，这些图像是从纽约市的25k Geo-Locations中良好的一致性和收集的。为了减轻大量像素的注释努力，我们提出了一个有效的街景图像注释管道，该管道利用了卫星视图的现有标签地图以及不同观点之间的转换关系（卫星，Panorama和Mono-View）。有了新的Omnicity数据集，我们为各种任务提供基准，包括构建足迹提取，高度估计以及构建平面/实例/细粒细分。我们还分析了视图对每个任务的影响，不同模型的性能，现有方法的局限性等。与现有的多层次和多视图基准相比，我们的Omnicity包含更多具有更丰富注释类型和更丰富的图像更多的视图，提供了从最先进的模型获得的更多基线结果，并为街道级全景图像中的细粒度建筑实例细分介绍了一项新颖的任务。此外，Omnicity为现有任务提供了新的问题设置，例如跨视图匹配，合成，分割，检测等，并促进开发新方法，以了解大规模的城市理解，重建和仿真。 Omnicity数据集以及基准将在https://city-super.github.io/omnicity上找到。

我们呈现MSEG，该数据集统一来自不同域的语义分段数据集。由于分类和注释实践不一致，因此，构成数据集的天真合并产生了差的表现。我们通过在超过80,000张图像中重新标记超过220,000个对象掩码，需要超过1.34年的集体注释员努力，调整分类管理并将像素级注释带标记为超过220,000个对象掩码。生成的复合数据集使训练单个语义分段模型可以有效地跨域功能并推广到培训期间未见的数据集。我们采用零拍摄的跨数据集转移作为基准，以系统地评估模型的稳健性，并表明MSEG培训与在没有所提出的贡献的数据集的单个数据集或天真混合的情况下，产生了大量更强大的模型。在MSEG培训的模型首先在Wilddash-V1排行榜上排名为强大的语义细分，在训练期间没有暴露于野生垃圾数据。我们在2020年的强大视觉挑战（RVC）中评估我们的模型，作为一个极端的泛化实验。 MSEG培训集中仅包括RVC中的七个数据集中中的三个;更重要的是，RVC的评估分类是不同的，更详细。令人惊讶的是，我们的模型显示出竞争性能并排名第二。为了评估我们对强大，高效和完整的场景理解的宏伟目的的关机，我们通过使用我们的数据集进行训练实例分段和Panoptic Seation模型超越语义分割。此外，我们还评估了各种工程设计决策和度量，包括分辨率和计算效率。虽然我们的模型远非这一隆重目标，但我们的综合评价对于进步至关重要。我们与社区分享所有模型和代码。

现有的计算机视觉系统可以与人类竞争，以理解物体的可见部分，但在描绘部分被遮挡物体的无形部分时，仍然远远远远没有达到人类。图像Amodal的完成旨在使计算机具有类似人类的Amodal完成功能，以了解完整的对象，尽管该对象被部分遮住。这项调查的主要目的是对图像Amodal完成领域的研究热点，关键技术和未来趋势提供直观的理解。首先，我们对这个新兴领域的最新文献进行了全面的评论，探讨了图像Amodal完成中的三个关键任务，包括Amodal形状完成，Amodal外观完成和订单感知。然后，我们检查了与图像Amodal完成有关的流行数据集及其共同的数据收集方法和评估指标。最后，我们讨论了现实世界中的应用程序和未来的研究方向，以实现图像的完成，从而促进了读者对现有技术和即将到来的研究趋势的挑战的理解。

理解世界的关键算法是材料分割，它将标签（金属，玻璃等）分配给每个像素。我们发现，在某些设置中对现有数据的培训培训的模型不足以解决这一模型，并在44,560个室内和室外图像上使用320万个密度段的大规模数据集来解决此问题，该图像比现有数据多23倍。我们的数据涵盖了更多样化的场景，对象，观点和材料，并包含皮肤类型的更公平的分布。我们表明，经过数据训练的模型优于数据集和观点的最先进模型。我们提出了一个大型场景解析基准和基线为0.729的每金精度，0.585平均级别准确度和46个材料的平均值为0.420。