传统上，分割任务是作为一个完整标签的像素分类任务提出的，可以从所有图像或视频共享的固定数量的预定义语义类别中预测每个像素的类。然而，遵循这种表述，在更现实的设置下，标准体系结构将不可避免地遇到各种挑战，其中类别的范围扩大了（例如，超出1K的级别）。另一方面，在典型的图像或视频中，只有少数类别，即存在一小部分完整标签。在本文中，我们提议将分割分解为两个子问题：（i）图像级或视频级多标签分类和（ii）像素级适应性选定标签分类。给定输入图像或视频，我们的框架首先在完整标签上进行多标签分类，然后对完整的标签进行分类，并根据其类置信度得分选择一个小子集。然后，我们使用等级自适应像素分类器对仅选择的标签执行像素的分类，该标签使用一组面向等级的可学习温度参数来调整像素分类分数。我们的方法在概念上是一般的，可以通过简单地使用轻质多标签分类头和等级适应像素分类器来改善各种现有的分割框架。我们通过四个任务进行了竞争性实验结果，证明了我们的框架的有效性，包括图像语义分割，图像泛型细分，视频实例分段和视频语义分段。尤其是，借助我们的rankSeg，Mask2Former在ADE20K PANOPTIC分段/YouTubevis 2019视频实例分段/VSPW视频语义分段基准分别获得了+0.8％/+0.7％/+0.7％。

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）。

In this paper, we study the context aggregation problem in semantic segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we present a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class. First, we learn object regions under the supervision of the ground-truth segmentation. Second, we compute the object region representation by aggregating the representations of the pixels lying in the object region. Last, we compute the relation between each pixel and each object region, and augment the representation of each pixel with the object-contextual representation which is a weighted aggregation of all the object region representations. We empirically demonstrate our method achieves competitive performance on various benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff. Our submission "HRNet + OCR + SegFix" achieves the 1 st place on the Cityscapes leaderboard by the ECCV 2020 submission deadline. Code is available at: https://git.io/openseg and https://git.io/HRNet.OCR.

图像分割是关于使用不同语义的分组像素，例如类别或实例成员身份，其中每个语义选择定义任务。虽然只有每个任务的语义不同，但目前的研究侧重于为每项任务设计专业架构。我们提出了蒙面关注掩模变压器（Mask2Former），这是一种能够寻址任何图像分段任务（Panoptic，实例或语义）的新架构。其关键部件包括屏蔽注意，通过限制预测掩模区域内的横向提取局部特征。除了将研究工作减少三次之外，它还优于四个流行的数据集中的最佳专业架构。最值得注意的是，Mask2Former为Panoptic semonation（Coco 57.8 PQ）设置了新的最先进的，实例分段（Coco上50.1 AP）和语义分割（ADE20K上的57.7 miou）。

Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks. To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. We first propose a task-conditioned joint training strategy that enables training on ground truths of each domain (semantic, instance, and panoptic segmentation) within a single multi-task training process. Secondly, we introduce a task token to condition our model on the task at hand, making our model task-dynamic to support multi-task training and inference. Thirdly, we propose using a query-text contrastive loss during training to establish better inter-task and inter-class distinctions. Notably, our single OneFormer model outperforms specialized Mask2Former models across all three segmentation tasks on ADE20k, CityScapes, and COCO, despite the latter being trained on each of the three tasks individually with three times the resources. With new ConvNeXt and DiNAT backbones, we observe even more performance improvement. We believe OneFormer is a significant step towards making image segmentation more universal and accessible. To support further research, we open-source our code and models at https://github.com/SHI-Labs/OneFormer

共同出现的视觉模式使上下文聚集成为语义分割的重要范式。现有的研究重点是建模图像中的上下文，同时忽略图像以下相应类别的有价值的语义。为此，我们提出了一个新颖的软采矿上下文信息，超出了名为McIbi ++的图像范式，以进一步提高像素级表示。具体来说，我们首先设置了动态更新的内存模块，以存储各种类别的数据集级别的分布信息，然后利用信息在网络转发过程中产生数据集级别类别表示。之后，我们为每个像素表示形式生成一个类概率分布，并以类概率分布作为权重进行数据集级上下文聚合。最后，使用汇总的数据集级别和传统的图像级上下文信息来增强原始像素表示。此外，在推论阶段，我们还设计了一种粗到最新的迭代推理策略，以进一步提高分割结果。 MCIBI ++可以轻松地纳入现有的分割框架中，并带来一致的性能改进。此外，MCIBI ++可以扩展到视频语义分割框架中，比基线进行了大量改进。配备MCIBI ++，我们在七个具有挑战性的图像或视频语义分段基准测试中实现了最先进的性能。

尽管有不同的相关框架，已经通过不同和专门的框架解决了语义，实例和Panoptic分段。本文为这些基本相似的任务提供了统一，简单，有效的框架。该框架，名为K-Net，段段由一组被学习内核持续一致，其中每个内核负责为潜在实例或填充类生成掩码。要解决区分各种实例的困难，我们提出了一个内核更新策略，使每个内核动态和条件在输入图像中的有意义的组上。 K-NET可以以结尾的方式培训，具有二分匹配，其培训和推论是自然的NMS和无框。没有钟声和口哨，K-Net超越了先前发表的全面的全面的单一模型，在ADE20K Val上的MS Coco Test-Dev分割和语义分割上分别与55.2％PQ和54.3％Miou分裂。其实例分割性能也与MS COCO上的级联掩模R-CNN相同，具有60％-90％的推理速度。代码和模型将在https://github.com/zwwwayne/k-net/发布。

Panoptic semonation涉及联合语义分割和实例分割的组合，其中图像内容分为两种类型：事物和东西。我们展示了Panoptic SegFormer，是与变压器的Panoptic Semonation的一般框架。它包含三个创新组件：高效的深度监督掩模解码器，查询解耦策略以及改进的后处理方法。我们还使用可变形的DETR来有效地处理多尺度功能，这是一种快速高效的DETR版本。具体而言，我们以层式方式监督掩模解码器中的注意模块。这种深度监督策略让注意模块快速关注有意义的语义区域。与可变形的DETR相比，它可以提高性能并将所需培训纪元的数量减少一半。我们的查询解耦策略对查询集的职责解耦并避免了事物和东西之间的相互干扰。此外，我们的后处理策略通过联合考虑分类和分割质量来解决突出的面具重叠而没有额外成本的情况。我们的方法会在基线DETR模型上增加6.2 \％PQ。 Panoptic SegFormer通过56.2 \％PQ实现最先进的结果。它还显示出对现有方法的更强大的零射鲁布利。代码释放\ url {https://github.com/zhiqi-li/panoptic-segformer}。

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at \url{https://github.com/IDEACVR/MaskDINO}.

全景部分分割（PPS）旨在将泛型分割和部分分割统一为一个任务。先前的工作主要利用分离的方法来处理事物，物品和部分预测，而无需执行任何共享的计算和任务关联。在这项工作中，我们旨在将这些任务统一在架构层面上，设计第一个名为Panoptic-Partformer的端到端统一方法。特别是，由于视觉变压器的最新进展，我们将事物，内容和部分建模为对象查询，并直接学会优化所有三个预测作为统一掩码的预测和分类问题。我们设计了一个脱钩的解码器，以分别生成零件功能和事物/东西功能。然后，我们建议利用所有查询和相应的特征共同执行推理。最终掩码可以通过查询和相应特征之间的内部产品获得。广泛的消融研究和分析证明了我们框架的有效性。我们的全景局势群体在CityScapes PPS和Pascal Context PPS数据集上实现了新的最新结果，至少有70％的GFLOPS和50％的参数降低。特别是，在Pascal上下文PPS数据集上采用SWIN Transformer后，我们可以通过RESNET50骨干链和10％的改进获得3.4％的相对改进。据我们所知，我们是第一个通过\ textit {统一和端到端变压器模型来解决PPS问题的人。鉴于其有效性和概念上的简单性，我们希望我们的全景贡献者能够充当良好的基准，并帮助未来的PPS统一研究。我们的代码和型号可在https://github.com/lxtgh/panoptic-partformer上找到。

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.

Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for semantic segmentation. In contrast to convolution-based methods, our approach allows to model global context already at the first layer and throughout the network. We build on the recent Vision Transformer (ViT) and extend it to semantic segmentation. To do so, we rely on the output embeddings corresponding to image patches and obtain class labels from these embeddings with a point-wise linear decoder or a mask transformer decoder. We leverage models pre-trained for image classification and show that we can fine-tune them on moderate sized datasets available for semantic segmentation. The linear decoder allows to obtain excellent results already, but the performance can be further improved by a mask transformer generating class masks. We conduct an extensive ablation study to show the impact of the different parameters, in particular the performance is better for large models and small patch sizes. Segmenter attains excellent results for semantic segmentation. It outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes.

零拍语义分割（ZS3）旨在分割培训中没有看到的新型类别。现有的作品将zs3作为像素级零拍分类问题，以及在仅使用文本预先培训的语言模型的帮助下，将语义知识从看见课程转移到未知一体。虽然简单，像素级ZS3配方显示了集成具有图像文本对预训练的视觉语言模型的有限能力，并且目前展示了愿景任务的巨大潜力。灵感来自观察，人类经常执行段级语义标签，我们建议将zs3分成两个子任务：1）将像素分组到段中的类别不可知的分组任务。 2）段的零拍分类任务。前者的子任务不涉及类别信息，可以直接传输到未安装类的组像素。后一子任务在段级执行，提供了一种自然的方式，可以利用预先培训的大规模视觉模型，用于ZS3的图像文本对（例如剪辑）。基于解耦制剂，我们提出了一种简单且有效的零拍语义分割模型，称为ZegFormer，这优于大幅边缘的先前方法，例如，Pascal VOC的35分和3分在Coco-在宫颈课程方面的东西。代码将在https://github.com/dingjiansw101/zegformer发布。

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.

最近建议的MaskFormer \ Cite {MaskFormer}对语义分割的任务提供了刷新的透视图：它从流行的像素级分类范例转移到蒙版级分类方法。实质上，它生成对应于类别段的配对概率和掩码，并在推理的分割映射期间结合它们。因此，分割质量依赖于查询如何捕获类别的语义信息及其空间位置。在我们的研究中，我们发现单尺度特征顶部的每个掩模分类解码器不足以提取可靠的概率或掩模。对于挖掘功能金字塔的丰富语义信息，我们提出了一个基于变压器的金字塔融合变压器（PFT），用于多尺度特征顶部的每个掩模方法语义分段。为了有效地利用不同分辨率的图像特征而不会产生过多的计算开销，PFT使用多尺度变压器解码器，具有跨尺度间间的关注来交换互补信息。广泛的实验评估和消融展示了我们框架的功效。特别是，与屏蔽Former相比，我们通过Reset-101c实现了3.2 miou改进了Reset-101c。此外，在ADE20K验证集上，我们的Swin-B骨架的结果与单尺度和多尺寸推断的屏蔽骨架中的较大的Swin-L骨架相匹配，分别实现54.1 miou和55.3 miou。使用Swin-L骨干，我们在ADE20K验证集中实现了56.0 Miou单尺度结果和57.2多尺度结果，从而获得数据集的最先进的性能。

一对一的匹配是DETR建立其端到端功能的关键设计，因此对象检测不需要手工制作的NMS（非最大抑制）方法来删除重复检测。这种端到端的签名对于DETR的多功能性很重要，并且已将其推广到广泛的视觉问题，包括实例/语义分割，人体姿势估计以及基于点云/多视图的检测，但是，我们注意到，由于分配为正样本的查询太少，因此一对一的匹配显着降低了阳性样品的训练效率。本文提出了一种基于混合匹配方案的简单而有效的方法，该方法将原始的一对一匹配分支与辅助查询结合在一起，这些查询在训练过程中使用一对一的匹配损失。该混合策略已被证明可显着提高训练效率并提高准确性。在推断中，仅使用原始的一对一匹配分支，从而维持端到端的优点和相同的DETR推断效率。该方法命名为$ \ MATHCAL {H} $ - DETR，它表明可以在各种视觉任务中始终如一地改进各种代表性的DITR方法，包括可变形，3DDER/PETRV2，PETR和TRANDRACK， ，其他。代码将在以下网址提供：https：//github.com/hdetr

In dense image segmentation tasks (e.g., semantic, panoptic), existing methods can hardly generalize well to unseen image domains, predefined classes, and image resolution & quality variations. Motivated by these observations, we construct a large-scale entity segmentation dataset to explore fine-grained entity segmentation, with a strong focus on open-world and high-quality dense segmentation. The dataset contains images spanning diverse image domains and resolutions, along with high-quality mask annotations for training and testing. Given the high-quality and -resolution nature of the dataset, we propose CropFormer for high-quality segmentation, which can improve mask prediction using high-res image crops that provide more fine-grained image details than the full image. CropFormer is the first query-based Transformer architecture that can effectively ensemble mask predictions from multiple image crops, by learning queries that can associate the same entities across the full image and its crop. With CropFormer, we achieve a significant AP gain of $1.9$ on the challenging fine-grained entity segmentation task. The dataset and code will be released at http://luqi.info/entityv2.github.io/.

我们介绍了一个高分辨率变压器（HRFormer），其学习了密集预测任务的高分辨率表示，与产生低分辨率表示的原始视觉变压器，具有高存储器和计算成本。我们利用在高分辨率卷积网络（HRNET）中引入的多分辨率并行设计，以及本地窗口自我关注，用于通过小型非重叠图像窗口进行自我关注，以提高存储器和计算效率。此外，我们将卷积介绍到FFN中以在断开连接的图像窗口中交换信息。我们展示了高分辨率变压器对人类姿态估计和语义分割任务的有效性，例如，HRFormer在Coco姿势估算中以$ 50 \％$ 50 + 50美元和30 \％$更少的拖鞋。代码可用：https://github.com/hrnet/hRFormer。

已知预测的集合，而是比单独采取的个体预测更好地执行更好。但是，对于需要重型计算资源的任务，\ texit {例如}语义细分，创建需要单独培训的学习者的集合几乎没有易行。在这项工作中，我们建议利用集合方法提供的性能提升，以增强语义分割，同时避免了集合的传统训练成本。我们的自我集成框架利用了通过特征金字塔网络方法生产的多尺度功能来提供独立解码器，从而在单个模型中创建集合。类似于集合，最终预测是每个学习者所做的预测的聚合。与以前的作品相比，我们的模型可以训练结束，减轻了传统的繁琐多阶段培训的合奏。我们的自身融合框架优于当前最先进的基准数据集ADE20K，Pascal Context和Coco-Stuff-10K用于语义细分，并且在城市景观竞争。代码将在Github.com/walbouss/senformer上使用。