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 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-

The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in

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.

我们介绍了一些源自摄影师的本地化数据集，他们实际上试图了解他们拍摄的图像中的视觉内容。它包括有4,500多个视觉障碍者拍摄的超过4,500张图像中的100个类别的近10,000个细分。与现有的少数弹射对象检测和实例分段数据集相比，我们的数据集是第一个在对象中找到孔（例如，在我们的分段的12.3 \％中找到），它显示的对象相对于占据相对于尺寸的范围较大。图像和文本在我们的对象中的常见五倍以上（例如，在我们的分割的22.4％中找到）。对三种现代少量定位算法的分析表明，它们概括为我们的新数据集。这些算法通常很难找到带有孔，非常小且非常大的物体以及缺乏文本的物体的对象。为了鼓励更大的社区致力于这些尚未解决的挑战，我们在https://vizwiz.org上公开分享了带注释的少数数据集。

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 .

In this paper we study the role of context in existing stateof-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of existing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.Comparisons to existing contextual datasets: Several datasets exist that have been labeled with contextual classes. Notable examples are the Barcelona [34], SIFT flow [25] and SUN [38] datasets. We now show that our PASCAL-

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.

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.

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.

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 .

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

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current nonensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.

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

脸部混淆（模糊，镶嵌等）已被证明对隐私保护有效；然而，对象识别研究通常假设访问完整的，无刺激的图像。在本文中，我们探讨了面部混淆对流行的Imagenet挑战视觉识别基准的影响。 ImageNet挑战中的大多数类别不是人类类别。但是，许多偶然的人出现在图像中，他们的隐私是一个问题。我们首先注释数据集中的面孔。然后，我们证明面部混淆对识别模型的准确性的影响最小。具体而言，我们在混淆图像上基准了多个深层神经网络，并观察到总体识别精度仅略有下降（<= 1.0％）。此外，我们尝试将学习转移到4个下游任务（对象识别，场景识别，面部属性分类和对象检测），并表明在混淆图像上学习的功能同样可以传递。我们的工作证明了隐私感知视觉识别的可行性，改善了高度使用的Imagenet挑战基准，并为将来的视觉数据集提出了重要的途径。数据和代码可从https://github.com/princetonvisualai/imagenet-face-obfuscation获得。

We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.

全球城市可免费获得大量的地理参考全景图像，以及各种各样的城市物体上的位置和元数据的详细地图。它们提供了有关城市物体的潜在信息来源，但是对象检测的手动注释是昂贵，费力和困难的。我们可以利用这种多媒体来源自动注释街道级图像作为手动标签的廉价替代品吗？使用Panorams框架，我们引入了一种方法，以根据城市上下文信息自动生成全景图像的边界框注释。遵循这种方法，我们仅以快速自动的方式从开放数据源中获得了大规模的（尽管嘈杂，但都嘈杂，但对城市数据集进行了注释。该数据集涵盖了阿姆斯特丹市，其中包括771,299张全景图像中22个对象类别的1400万个嘈杂的边界框注释。对于许多对象，可以从地理空间元数据（例如建筑价值，功能和平均表面积）获得进一步的细粒度信息。这样的信息将很难（即使不是不可能）单独根据图像来获取。为了进行详细评估，我们引入了一个有效的众包协议，用于在全景图像中进行边界框注释，我们将其部署以获取147,075个地面真实对象注释，用于7,348张图像的子集，Panorams-clean数据集。对于我们的Panorams-Noisy数据集，我们对噪声以及不同类型的噪声如何影响图像分类和对象检测性能提供了广泛的分析。我们可以公开提供数据集，全景噪声和全景清洁，基准和工具。

在本文中，我们提出了一种与渔业相关数据的方法，该方法使我们能够通过多个可以利用众包接口的培训和生产循环在数据集上迭代标记的图像数据集。我们将算法及其结果介绍在使用海底自动水下车辆收集的两组单独的图像数据上。第一个数据集由2,026个完全未标记的图像组成，而第二个数据集由21,968张图像组成，这些图像由专家注释。我们的结果表明，使用小子集进行培训，并迭代以构建较大的标记数据，从而使我们能够收敛到带有少量迭代的完全注释数据集。即使在专家标记的数据集的情况下，该方法论的单个迭代也通过发现与鱼层相关的鱼类相关标签的其他复杂示例，也很小，或者被与水下图像相关的对比度限制所掩盖，从而改善了标签。

海洋正在经历前所未有的快速变化，在负责任管理所需的时空尺度上，视觉监测海洋生物群是一项艰巨的任务。由于研究界寻求基准，因此所需的数据收集的数量和速率迅速超过了我们处理和分析它们的能力。机器学习的最新进展可以对视觉数据进行快速，复杂的分析，但由于缺乏数据标准化，格式不足以及对大型标签数据集的需求，在海洋中取得了有限的成功。为了满足这一需求，我们构建了Fathomnet，这是一个开源图像数据库，该数据库标准化和汇总了经过精心策划的标记数据。 Fathomnet已被海洋动物，水下设备，碎片和其他概念的现有标志性和非偶像图像所播种，并允许分布式数据源的未来贡献。我们展示了如何使用Fathomnet数据在其他机构视频上训练和部署模型，以减少注释工作，并在与机器人车辆集成时启用自动跟踪水下概念。随着Fathomnet继续增长并结合了社区的更多标记数据，我们可以加速视觉数据以实现健康且可持续的全球海洋。