点云的几乎没有分割仍然是一项具有挑战性的任务，因为没有有效的方法将局部点云信息转换为全局表示，这阻碍了点特征的概括能力。在这项研究中，我们提出了双向特征全球化（BFG）方法，该方法利用点特征和原型向量之间的相似性测量，以双向方式将全球感知嵌入到局部点特征中。随着点对点型全球化（PO2PRG），BFG根据从密度点特征到稀疏原型的相似权重将本地点特征汇总到原型。使用原型到点全球化（PR2POG），基于从稀疏原型到密集点特征的相似性权重，全局感知嵌入到局部点特征中。每个类嵌入全局感知的类的稀疏原型汇总到基于度量学习框架的几个原型3D分割的单个原型。对S3DIS和SCANNET的广泛实验表明，BFG显着超过了最新方法。

Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing methods either focus on the restrictive setting of one-way few-shot segmentation or suffer from incomplete coverage of object regions. In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes, capable of capturing diverse and fine-grained object features. In addition, we propose to leverage unlabeled data to enrich our part-aware prototypes, resulting in better modeling of intra-class variations of semantic objects. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin.

几次拍摄的语义分割旨在将新颖的类对象分段为仅具有少数标记的支持图像。大多数高级解决方案利用度量学习框架，通过将每个查询功能与学习的类特定的原型匹配来执行分段。然而，由于特征比较不完整，该框架遭受了偏见的分类。为了解决这个问题，我们通过引入类别特定的和类别不可知的原型来提出自适应原型表示，从而构建与查询功能学习语义对齐的完整样本对。互补特征学习方式有效地丰富了特征比较，并有助于在几次拍摄设置中产生一个非偏见的分段模型。它用双分支端到端网络（\即，特定于类分支和类别不可知分支）实现，它生成原型，然后组合查询特征以执行比较。此外，所提出的类别无神不可话的分支简单而且有效。在实践中，它可以自适应地为查询图像生成多种类别 - 不可知的原型，并以自我对比方式学习特征对齐。广泛的Pascal-5 $ ^ i $和Coco-20 $ ^ i $展示了我们方法的优越性。在不牺牲推理效率的费用中，我们的模型实现了最先进的，导致1-Shot和5-Shot Settings进行语义分割。

Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most existing studies use Proto-typical Networks (PN) and have obtained compelling success. However, these approaches overlook the query image features extracted from the proposed representation network, failing to preserving the spatial connection between query and support images. In this paper, we propose a novel self-supervised few-shot medical image segmentation network and introduce a novel Cycle-Resemblance Attention (CRA) module to fully leverage the pixel-wise relation between query and support medical images. Notably, we first line up multiple attention blocks to refine more abundant relation information. Then, we present CRAPNet by integrating the CRA module with a classic prototype network, where pixel-wise relations between query and support features are well recaptured for segmentation. Extensive experiments on two different medical image datasets, e.g., abdomen MRI and abdomen CT, demonstrate the superiority of our model over existing state-of-the-art methods.

很少有语义细分旨在识别一个看不见类别的对象区域，只有几个带注释的示例作为监督。几次分割的关键是在支持图像和查询图像之间建立牢固的语义关系，并防止过度拟合。在本文中，我们提出了一个有效的多相似性超关联网络（MSHNET），以解决几个射击语义分割问题。在MSHNET中，我们提出了一种新的生成原型相似性（GPS），与余弦相似性可以在支持图像和查询图像之间建立牢固的语义关系。基于全局特征的本地生成的原型相似性在逻辑上与基于本地特征的全局余弦相似性互补，并且可以通过同时使用两个相似性来更全面地表达查询图像和受支持图像之间的关系。此外，我们提出了MSHNET中的对称合并块（SMB），以有效合并多层，多弹射和多相似性超相关特征。 MSHNET是基于相似性而不是特定类别特征而构建的，这些特征可以实现更一般的统一性并有效地减少过度拟合。在两个基准的语义分割数据集Pascal-5i和Coco-20i上，MSHNET在1次和5次语义分段任务上实现了新的最先进的表演。

Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This paper considers few-shot 3D point cloud object detection, where only a few annotated samples of novel classes are needed with abundant samples of base classes. To this end, we propose Prototypical VoteNet to recognize and localize novel instances, which incorporates two new modules: Prototypical Vote Module (PVM) and Prototypical Head Module (PHM). Specifically, as the 3D basic geometric structures can be shared among categories, PVM is designed to leverage class-agnostic geometric prototypes, which are learned from base classes, to refine local features of novel categories.Then PHM is proposed to utilize class prototypes to enhance the global feature of each object, facilitating subsequent object localization and classification, which is trained by the episodic training strategy. To evaluate the model in this new setting, we contribute two new benchmark datasets, FS-ScanNet and FS-SUNRGBD. We conduct extensive experiments to demonstrate the effectiveness of Prototypical VoteNet, and our proposed method shows significant and consistent improvements compared to baselines on two benchmark datasets.

现有的少数射击分段方法基于支持 - 引人入胜的匹配框架取得了巨大进展。但是，他们仍然受到所提供的少量支撑的覆盖率有限的覆盖范围。由简单的格式塔原理激励，即属于同一对象的像素比同一班级的不同对象的像素更相似，我们提出了一种新颖的自支撑匹配策略来减轻此问题，该策略使用查询原型来匹配查询功能查询原型是从高信心查询预测中收集的。该策略可以有效地捕获查询对象的一致潜在特性，从而符合查询功能。我们还提出了一个自适应的自支持背景原型生成模块和自支撑损失，以进一步促进自支撑匹配过程。我们的自支撑网络大大提高了原型质量，更强的骨架和更多支持，并在多个数据集上实现了SOTA。代码位于\ url {https://github.com/fanq15/ssp}。

3D零件分割是高级CAM/CAD工作流程中的重要步骤。精确的3D细分有助于降低制造设备（例如计算机控制的CNC）生产的工作配件的缺陷率，从而提高了工作效率并获得了随之而来的经济利益。在3D模型分割上进行的大量现有作品主要基于完全监督的学习，该学习训练AI模型具有大型，带注释的数据集。但是，缺点是，完全监督的学习方法的最终模型高度依赖于可用数据集的完整性，并且其概括能力对新的未知细分类型（即其他新颖的类别）相对较差。在这项工作中，我们提出并开发了一种值得注意的基于学习的方法，以在CAM/CAD中进行有效的部分分割；这旨在显着增强其概括能力，并通过仅使用相对较少的样本灵活地适应新的分割任务。结果，它不仅减少了通常无法实现和详尽的监督数据集完整性的要求，而且还提高了对现实世界应用程序的灵活性。作为进一步的改进和创新，我们还采用了网络中的转换网和中心损失块。这些特征有助于提高整个工作人员各种可能实例的3D特征的理解，并确保在特征空间中同一类的密切分布。此外，我们的方法以降低空间消耗的点云格式存储数据，并且还使所涉及的各种过程变得更加容易阅读和编辑访问（从而提高了效率和有效性并降低了成本）。

以前的人类解析模型仅限于将人类解析为预定义的类，这对于通常具有新时尚项目类的实用时尚应用是不灵活的。在本文中，我们定义了一个新颖的单次人类解析（OSHP）任务，该任务需要将人解析为任何测试示例定义的一组开放式类别。在培训期间，仅公开基础课程，这仅与一部分测试时间类别重叠。为了解决OSHP中的三个主要挑战，即小型，测试偏见和类似部分，我们设计了一个端到端的一击人类解析网络（EOP-NET）。首先，提出了一个端到端的人解析框架，以将查询图像解析为粗粒和细粒度的人类类别，该框架建立了一个强大的嵌入网络，具有在不同粒度上共享的丰富语义信息，从人类阶级。然后，我们通过逐步平滑训练时间静态原型来提出学习势头更新的原型，这有助于稳定训练并学习健壮的功能。此外，我们设计了一种双重度量学习方案，该方案鼓励网络增强特征的表示能力和可传递性。因此，我们的EOP-NET可以学习代表性功能，这些功能可以快速适应新颖的类并减轻测试偏置问题。此外，我们在原型水平上采用了对比损失，从而在细粒度度量空间中执行了类别之间的距离，以区分相似的部分。我们根据OSHP任务量身定制了三个现有的人类解析基准。新基准测试的实验表明，EOP-NET的表现优于大量边缘的代表性单次分割模型，这是进一步研究这项新任务的强大基线。源代码可从https://github.com/charleshhy/one-shot-human-parsing获得。

很少有分割的目的是仅给出少数标记的样品，旨在细分看不见的级对象。原型学习，支持功能通过平均全局和局部对象信息产生单个原型，在FSS中已广泛使用。但是，仅利用原型矢量可能不足以代表所有训练数据的功能。为了提取丰富的特征并做出更精确的预测，我们提出了一个多相似性和注意力网络（MSANET），包括两个新型模块，一个多相似性模块和一个注意模块。多相似模块利用支持图像和查询图像的多个特征图来估计准确的语义关系。注意模块指示网络专注于相关的信息。该网络在标准FSS数据集，Pascal-5i 1-Shot，Pascal-5i 5-Shot，Coco-20i 1-Shot和Coco-20i 5-Shot上进行了测试。具有RESNET-101骨架的MSANET可在所有4基准测试数据集中达到最先进的性能，而平均交叉点（MIOU）为69.13％，73.99％，51.09％，56.80％。代码可在https://github.com/aivresearch/msanet上获得

本文在3D Point Cloud中介绍了一个新问题：很少示例实例分割。给定一些带注释的点云举例说明了目标类，我们的目标是在查询点云中细分该目标类的所有实例。这个问题具有广泛的实用应用，在重点实例分段注释非常昂贵的收集中。为了解决此问题，我们提出了测量形式 - 第一个用于3D点云实例分割的地球引导变压器。关键的想法是利用大地距离来应对LIDAR 3D点云的密度不平衡。 LIDAR 3D点云在物体表面附近茂密，在其他地方稀疏或空，使欧几里得距离较差以区分不同的物体。另一方面，大地测量距离更合适，因为它编码了场景的几何形状，该几何形状可以用作变压器解码器中注意机制的指导信号，以生成代表实例的不同特征的内核。然后将这些内核用于动态卷积以获得最终实例掩模。为了评估新任务上的测量形式，我们提出了两个常见的3D点云实例分割数据集的新拆分：ScannETV2和S3DIS。地球形式始终优于根据最新的3D点云实例分割方法的强大基线，并具有明显的余量。代码可从https://github.com/vinairesearch/geoformer获得。

Despite the remarkable success of existing methods for few-shot segmentation, there remain two crucial challenges. First, the feature learning for novel classes is suppressed during the training on base classes in that the novel classes are always treated as background. Thus, the semantics of novel classes are not well learned. Second, most of existing methods fail to consider the underlying semantic gap between the support and the query resulting from the representative bias by the scarce support samples. To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation. In the feature encoding stage, we design the Semantic-Preserving Feature Learning module (SPFL) to first exploit and then retain the latent semantics contained in the whole input image, especially those in the background that belong to novel classes. In the prediction stage for segmentation, we learn an Self-Refined Online Foreground-Background classifier (SROFB), which is able to refine itself using the high-confidence pixels of query image to facilitate its adaptation to the query image and bridge the support-query semantic gap. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ datasets demonstrates the advantages of these two novel designs both quantitatively and qualitatively.

Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation from only a few annotated examples. In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set. Our PANet learns classspecific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes. With non-parametric metric learning, PANet offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. Moreover, PANet introduces a prototype alignment regularization between support and query. With this, PANet fully exploits knowledge from the support and provides better generalization on few-shot segmentation. Significantly, our model achieves the mIoU score of 48.1% and 55.7% on PASCAL-5 i for 1-shot and 5-shot settings respectively, surpassing the state-of-the-art method by 1.8% and 8.6%.

我们解决了几次拍摄语义分割（FSS）的问题，该问题旨在通过一些带有一些注释的样本分段为目标图像中的新型类对象。尽管通过结合基于原型的公制学习来进行最近的进步，但由于其特征表示差，现有方法仍然显示出在极端内部对象变化和语义相似的类别对象下的有限性能。为了解决这个问题，我们提出了一种针对FSS任务定制的双重原型对比学习方法，以有效地捕获代表性的语义。主要思想是通过增加阶级距离来鼓励原型更差异，同时减少了原型特征空间中的课堂距离。为此，我们首先向类别特定的对比丢失丢失具有动态原型字典，该字典字典存储在训练期间的类感知原型，从而实现相同的类原型和不同的类原型是不同的。此外，我们通过压缩每集内语义类的特征分布来提高类别无话的对比损失，以提高未经看不见的类别的概念能力。我们表明，所提出的双重原型对比学习方法优于Pascal-5i和Coco-20i数据集的最先进的FSS方法。该代码可用于：https：//github.com/kwonjunn01/dpcl1。

Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated support image(s) and is matched to features representing each pixel in the query image. However, models learnt in this way are insufficiently discriminatory, and often produce false positives: misclassifying background pixels as foreground. Some FSS methods try to address this issue by using the background in the support image(s) to help identify the background in the query image. However, the backgrounds of theses images is often quite distinct, and hence, the support image background information is uninformative. This article proposes a method, QSR, that extracts the background from the query image itself, and as a result is better able to discriminate between foreground and background features in the query image. This is achieved by modifying the training process to associate prototypes with class labels including known classes from the training data and latent classes representing unknown background objects. This class information is then used to extract a background prototype from the query image. To successfully associate prototypes with class labels and extract a background prototype that is capable of predicting a mask for the background regions of the image, the machinery for extracting and using foreground prototypes is induced to become more discriminative between different classes. Experiments for both 1-shot and 5-shot FSS on both the PASCAL-5i and COCO-20i datasets demonstrate that the proposed method results in a significant improvement in performance for the baseline methods it is applied to. As QSR operates only during training, these improved results are produced with no extra computational complexity during testing.

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.

Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption that the training and testing data are of the same distribution, which may suffer from performance degradation when evaluated on the out-of-distribution scenes. To alleviate the annotation cost and the performance degradation, this paper introduces the synthetic-to-real domain generalization setting to this task. Specifically, the domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. To address these problems, we first propose a clustering instance mix (CINMix) augmentation technique to diversify the layouts of the source data. In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns. The multi-prototypes can model the intra-class variance and rectify the global classifier in both training and inference stages. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap and thus improve the generalization ability on real-world datasets.

具有很少带注释的样本的训练语义分割模型在各种现实世界中具有巨大的潜力。对于少数拍摄的分段任务，主要的挑战是如何准确地测量使用有限的培训数据之间的支持样本和查询样品之间的语义对应关系。为了解决这个问题，我们建议用可变形的4D变压器汇总可学习的协方差矩阵，以有效预测分割图。具体而言，在这项工作中，我们首先设计了一种新颖的艰难示例挖掘机制，以学习高斯过程的协方差内核。在对应测量中，学到的协方差内核函数比现有基于余弦相似性的方法具有很大的优势。基于学到的协方差内核，设计有效的双重变形4D变压器模块旨在适应骨料特征相似性图中的分割结果。通过组合这两种设计，提出的方法不仅可以在公共基准测试上设置新的最新性能，而且比现有方法更快地收敛。三个公共数据集的实验证明了我们方法的有效性。

深度学习极大地提高了语义细分的性能，但是，它的成功依赖于大量注释的培训数据的可用性。因此，许多努力致力于域自适应语义分割，重点是将语义知识从标记的源域转移到未标记的目标域。现有的自我训练方法通常需要多轮训练，而基于对抗训练的另一个流行框架已知对超参数敏感。在本文中，我们提出了一个易于训练的框架，该框架学习了域自适应语义分割的域不变原型。特别是，我们表明域的适应性与很少的学习共享一个共同的角色，因为两者都旨在识别一些从大量可见数据中学到的知识的看不见的数据。因此，我们提出了一个统一的框架，用于域适应和很少的学习。核心思想是使用从几个镜头注释的目标图像中提取的类原型来对源图像和目标图像的像素进行分类。我们的方法仅涉及一个阶段训练，不需要对大规模的未经通知的目标图像进行培训。此外，我们的方法可以扩展到域适应性和几乎没有射击学习的变体。关于适应GTA5到CITYSCAPES和合成景观的实验表明，我们的方法实现了对最先进的竞争性能。

Semantic segmentation assigns a class label to each image pixel. This dense prediction problem requires large amounts of manually annotated data, which is often unavailable. Few-shot learning aims to learn the pattern of a new category with only a few annotated examples. In this paper, we formulate the few-shot semantic segmentation problem from 1-way (class) to N-way (classes). Inspired by few-shot classification, we propose a generalized framework for few-shot semantic segmentation with an alternative training scheme. The framework is based on prototype learning and metric learning. Our approach outperforms the baselines by a large margin and shows comparable performance for 1-way few-shot semantic segmentation on PASCAL VOC 2012 dataset.