We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We then propose an interactive, scribble-based annotation framework.

Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most userfriendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCAL-CONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at http://research.microsoft.com/en-us/um/ people/jifdai/downloads/scribble_sup.

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.

大多数现有的点云实例和语义分割方法在很大程度上依赖于强大的监督信号，这需要场景中每个点的点级标签。但是，这种强大的监督遭受了巨大的注释成本，引起了研究有效注释的需求。在本文中，我们发现实例的位置对实例和语义3D场景细分都很重要。通过充分利用位置，我们设计了一种弱监督的点云分割算法，该算法仅需要单击每个实例以指示其注释的位置。通过进行预处理过度分割，我们将这些位置注释扩展到seg级标签中。我们通过将未标记的片段分组分组到相关的附近标签段中，进一步设计一个段分组网络（SEGGROUP），以在SEG级标签下生成点级伪标签，以便现有的点级监督的分段模型可以直接消耗这些PSEUDO标签为了训练。实验结果表明，我们的SEG级监督方法（SEGGROUP）通过完全注释的点级监督方法获得了可比的结果。此外，在固定注释预算的情况下，它的表现优于最近弱监督的方法。

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

视频分割，即将视频帧分组到多个段或对象中，在广泛的实际应用中扮演关键作用，例如电影中的视觉效果辅助，自主驾驶中的现场理解，以及视频会议中的虚拟背景创建，名称一些。最近，由于计算机愿景中的联系复兴，一直存在众多深度学习的方法，这一直专用于视频分割并提供引人注目的性能。在这项调查中，通过引入各自的任务设置，背景概念，感知需要，开发历史，以及开发历史，综合审查这一领域的两种基本研究，即在视频和视频语义分割中，即视频和视频语义分割中的通用对象分段（未知类别）。主要挑战。我们还提供关于两种方法和数据集的代表文学的详细概述。此外，我们在基准数据集中呈现了审查方法的定量性能比较。最后，我们指出了这一领域的一套未解决的开放问题，并提出了进一步研究的可能机会。

在这项工作中，我们专注于互动人类解析（IHP），旨在将人体形象分成多个人体部位，具有来自用户的相互作用的指导。这项新任务继承了人类解析的类感知属性，其无法通过通常是禁止类别的传统交互式图像分割方法很好地解决。为了解决这项新任务，我们首先利用用户点击以识别给定图像中的不同人为部分。随后将这些点击转换为语义感知的本地化映射，其与RGB图像连接以形成分割网络的输入并生成初始解析结果。为了使网络能够更好地了解用户在校正过程中的目的，我们调查了改进的几个主要方法，并揭示了基于随机采样的点击增强是推广校正效果的最佳方式。此外，我们还提出了一种语义感知损失（SP损失）来增加培训，这可以有效利用点击的语义关系以获得更好的优化。为了最好的知识，这项工作是第一次尝试在交互式设置下解决人类解析任务。我们的IHP解决方案在基准嘴唇上实现了85 \％Miou，Pascal-Person-Part和CiHP，75 \％Miou，只有1.95,3.02,2.84和每班3.09点击的Helen。这些结果表明，我们只需几个人类努力就可以获得高品质的人类解析面具。我们希望这项工作能够激励更多的研究人员在未来为IHP开发数据有效的解决方案。

现代方法通常将语义分割标记为每个像素分类任务，而使用替代掩码分类处理实例级分割。我们的主要洞察力：掩码分类是足够的一般，可以使用完全相同的模型，丢失和培训过程来解决语义和实例级分段任务。在此观察之后，我们提出了一个简单的掩模分类模型，该模型预测了一组二进制掩码，每个模型与单个全局类标签预测相关联。总的来说，所提出的基于掩模分类的方法简化了语义和Panoptic分割任务的有效方法的景观，并显示出优异的经验结果。特别是，当类的数量大时，我们观察到掩码形成器优于每个像素分类基线。我们的面具基于分类的方法优于当前最先进的语义（ADE20K上的55.6 miou）和Panoptic Seation（Coco）模型的Panoptic Seationation（52.7 PQ）。

为视频中的每个像素分配语义类和跟踪身份的任务称为视频Panoptic分段。我们的工作是第一个在真实世界中瞄准这项任务，需要在空间和时间域中的密集解释。由于此任务的地面真理难以获得，但是，现有数据集是合成构造的或仅在短视频剪辑中稀疏地注释。为了克服这一点，我们介绍了一个包含两个数据集，Kitti-Step和Motchallenge步骤的新基准。数据集包含长视频序列，提供具有挑战性的示例和用于研究长期像素精确分割和在真实条件下跟踪的测试床。我们进一步提出了一种新的评估度量分割和跟踪质量（STQ），其相当余额平衡该任务的语义和跟踪方面，并且更适合评估任意长度的序列。最后，我们提供了几个基线来评估此新具有挑战性数据集的现有方法的状态。我们已将我们的数据集，公制，基准服务器和基准公开提供，并希望这将激发未来的研究。

In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network, we first design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously. More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification. It first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolve the conflicts between semantic and instance segmentation. Additionally, it handles the challenge caused by the varying number of instances and permits back propagation to the bottom modules in an end-to-end manner. Extensive experimental results on Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves stateof-the-art performance with much faster inference. Code has been made available at: https://github.com/ uber-research/UPSNet. * Equal contribution.† This work was done when Hengshuang Zhao was an intern at Uber ATG.

We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation.

深度学习的快速发展在分割方面取得了长足的进步，这是计算机视觉的基本任务之一。但是，当前的细分算法主要取决于像素级注释的可用性，这些注释通常昂贵，乏味且费力。为了减轻这一负担，过去几年见证了越来越多的关注，以建立标签高效，深度学习的细分算法。本文对标签有效的细分方法进行了全面的审查。为此，我们首先根据不同类型的弱标签提供的监督（包括没有监督，粗略监督，不完整的监督和嘈杂的监督和嘈杂的监督），首先开发出一种分类法来组织这些方法，并通过细分类型（包括语义细分）补充，实例分割和全景分割）。接下来，我们从统一的角度总结了现有的标签有效的细分方法，该方法讨论了一个重要的问题：如何弥合弱监督和密集预测之间的差距 - 当前的方法主要基于启发式先导，例如交叉像素相似性，跨标签约束，跨视图一致性，跨图像关系等。最后，我们分享了对标签有效深层细分的未来研究方向的看法。

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ∼ 95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025 × 2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several topdown approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation.

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.

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。

Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with humanannotated, pixel-level segmentation masks. Such pixelaccurate supervision demands expensive labeling effort and limits the performance of deep networks that usually benefit from more training data. In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations. The basic idea is to iterate between automatically generating region proposals and training convolutional networks. These two steps gradually recover segmentation masks for improving the networks, and vise versa. Our method, called "BoxSup", produces competitive results (e.g., 62.0% mAP for validation) supervised by boxes only, on par with strong baselines (e.g., 63.8% mAP) fully supervised by masks under the same setting. By leveraging a large amount of bounding boxes, BoxSup further unleashes the power of deep convolutional networks and yields state-of-the-art results on PAS-CAL VOC 2012 and PASCAL-CONTEXT [24].

尽管交互式图像分割方法的进展情况，但高质量的像素级注释仍然耗时和费力 - 几个深度学习应用的瓶颈。我们逐步回到由特征空间投影引导的多个图像提出的互动和同时段注释。该策略与现有的交互式分段方法呈现出与现有的交互式分段方法相比，该方法在图像域中进行注释。我们表明要素空间注释在前景分段数据集中使用最先进的方法实现了竞争结果：ICOSEG，DAVIS和屋顶。此外，在语义分割上下文中，它在CityScapes数据集中实现了91.5 \％的准确性，比原始注释程序快74.75倍.Further，我们的贡献揭示了可以与现有方法集成的新颖方向上的灯光。补充材料呈现视频演示。代码在https://github.com/lids-unicamp/rethinking-interactive-image-egation。

The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-ofthe-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, topperforming method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation.

Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.