本文制定了一个新问题，实例影子检测，旨在检测影子实例和关联的对象实例，这些实例在输入图像中投射每个阴影。为了完成此任务，我们首先编译了一个新的数据集，其中包含掩码，用于影子实例，对象实例和阴影对象关联。然后，我们设计了一个评估度量，以定量评估实例阴影检测的性能。此外，我们设计了一个单阶段检测器，以端到端的方式执行实例阴影检测，其中双向关系学习模块和可变形的maskiou头在检测器中提议直接学习阴影实例与对象实例之间的关系并提高预测口罩的准确性。最后，我们在实例阴影检测的基准数据集上进行定量和定性评估我们的方法，并在光方向估计和照片编辑中显示我们方法的适用性。

分割高度重叠的图像对象是具有挑战性的，因为图像上的真实对象轮廓和遮挡边界之间通常没有区别。与先前的实例分割方法不同，我们将图像形成模拟为两个重叠层的组成，并提出了双层卷积网络（BCNET），其中顶层检测到遮挡对象（遮挡器），而底层则渗透到部分闭塞实例（胶囊）。遮挡关系与双层结构的显式建模自然地将遮挡和遮挡实例的边界解散，并在掩模回归过程中考虑了它们之间的相互作用。我们使用两种流行的卷积网络设计（即完全卷积网络（FCN）和图形卷积网络（GCN））研究了双层结构的功效。此外，我们通过将图像中的实例表示为单独的可学习封闭器和封闭者查询，从而使用视觉变压器（VIT）制定双层解耦。使用一个/两个阶段和基于查询的对象探测器具有各种骨架和网络层选择验证双层解耦合的概括能力，如图像实例分段基准（可可，亲戚，可可）和视频所示实例分割基准（YTVIS，OVIS，BDD100K MOTS），特别是对于重闭塞病例。代码和数据可在https://github.com/lkeab/bcnet上找到。

We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation). Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations (typically ROIPool or ROIAlign) to obtain the final instance masks. In contrast, we propose to solve instance segmentation from a new perspective. Instead of using instancewise ROIs as inputs to a network of fixed weights, we employ dynamic instance-aware networks, conditioned on instances. CondInst enjoys two advantages: 1) Instance segmentation is solved by a fully convolutional network, eliminating the need for ROI cropping and feature alignment.2) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed. On the COCO dataset, we outperform a few recent methods including welltuned Mask R-CNN baselines, without longer training schedules needed.

玻璃在我们的日常生活中非常普遍。现有的计算机视觉系统忽略了它，因此可能会产生严重的后果，例如，机器人可能会坠入玻璃墙。但是，感知玻璃的存在并不简单。关键的挑战是，任意物体/场景可以出现在玻璃后面。在本文中，我们提出了一个重要的问题，即从单个RGB图像中检测玻璃表面。为了解决这个问题，我们构建了第一个大规模玻璃检测数据集（GDD），并提出了一个名为GDNet-B的新颖玻璃检测网络，该网络通过新颖的大型场探索大型视野中的丰富上下文提示上下文特征集成（LCFI）模块并将高级和低级边界特征与边界特征增强（BFE）模块集成在一起。广泛的实验表明，我们的GDNET-B可以在GDD测试集内外的图像上达到满足玻璃检测结果。我们通过将其应用于其他视觉任务（包括镜像分割和显着对象检测）来进一步验证我们提出的GDNET-B的有效性和概括能力。最后，我们显示了玻璃检测的潜在应用，并讨论了可能的未来研究方向。

我们介绍了一种名为RobustAbnet的新表检测和结构识别方法，以检测表的边界并从异质文档图像中重建每个表的细胞结构。为了进行表检测，我们建议将Cornernet用作新的区域建议网络来生成更高质量的表建议，以更快的R-CNN，这显着提高了更快的R-CNN的定位准确性以进行表检测。因此，我们的表检测方法仅使用轻巧的RESNET-18骨干网络，在三个公共表检测基准（即CTDAR TRACKA，PUBLAYNET和IIIT-AR-13K）上实现最新性能。此外，我们提出了一种新的基于分裂和合并的表结构识别方法，其中提出了一个新型的基于CNN的新空间CNN分离线预测模块将每个检测到的表分为单元格，并且基于网格CNN的CNN合并模块是应用用于恢复生成细胞。由于空间CNN模块可以有效地在整个表图像上传播上下文信息，因此我们的表结构识别器可以坚固地识别具有较大的空白空间和几何扭曲（甚至弯曲）表的表。得益于这两种技术，我们的表结构识别方法在包括SCITSR，PubTabnet和CTDAR TrackB2-Modern在内的三个公共基准上实现了最先进的性能。此外，我们进一步证明了我们方法在识别具有复杂结构，大空间以及几何扭曲甚至弯曲形状的表上的表格上的优势。

大多数最先进的实例级人类解析模型都采用了两阶段的基于锚的探测器，因此无法避免启发式锚盒设计和像素级别缺乏分析。为了解决这两个问题，我们设计了一个实例级人类解析网络，该网络在像素级别上无锚固且可解决。它由两个简单的子网络组成：一个用于边界框预测的无锚检测头和一个用于人体分割的边缘引导解析头。无锚探测器的头继承了像素样的优点，并有效地避免了对象检测应用中证明的超参数的敏感性。通过引入部分感知的边界线索，边缘引导的解析头能够将相邻的人类部分与彼此区分开，最多可在一个人类实例中，甚至重叠的实例。同时，利用了精炼的头部整合盒子级别的分数和部分分析质量，以提高解析结果的质量。在两个多个人类解析数据集（即CIHP和LV-MHP-V2.0）和一个视频实例级人类解析数据集（即VIP）上进行实验，表明我们的方法实现了超过全球级别和实例级别的性能最新的一阶段自上而下的替代方案。

本文介绍了端到端的实例分段框架，称为SOIT，该段具有实例感知变压器的段对象。灵感来自Detr〜\ Cite {carion2020end}，我们的方法视图实例分段为直接设置预测问题，有效地消除了对ROI裁剪，一对多标签分配等许多手工制作组件的需求，以及非最大抑制（ nms）。在SOIT中，通过在全局图像上下文下直接地将多个查询直接理解语义类别，边界框位置和像素 - WISE掩码的一组对象嵌入。类和边界盒可以通过固定长度的向量轻松嵌入。尤其是由一组参数嵌入像素方面的掩模以构建轻量级实例感知变压器。之后，实例感知变压器产生全分辨率掩码，而不涉及基于ROI的任何操作。总的来说，SOIT介绍了一个简单的单级实例分段框架，它是无乐和NMS的。 MS Coco DataSet上的实验结果表明，优于最先进的实例分割显着的优势。此外，在统一查询嵌入中的多个任务的联合学习还可以大大提高检测性能。代码可用于\ url {https://github.com/yuxiaodonghri/soit}。

现有的实例分割方法已经达到了令人印象深刻的表现，但仍遭受了共同的困境：一个实例推断出冗余表示（例如，多个框，网格和锚点），这导致了多个重复的预测。因此，主流方法通常依赖于手工设计的非最大抑制（NMS）后处理步骤来选择最佳预测结果，这会阻碍端到端训练。为了解决此问题，我们建议一个称为Uniinst的无盒和无端机实例分割框架，该框架仅对每个实例产生一个唯一的表示。具体而言，我们设计了一种实例意识到的一对一分配方案，即仅产生一个表示（Oyor），该方案根据预测和地面真相之间的匹配质量，动态地为每个实例动态分配一个独特的表示。然后，一种新颖的预测重新排列策略被优雅地集成到框架中，以解决分类评分和掩盖质量之间的错位，从而使学习的表示形式更具歧视性。借助这些技术，我们的Uniinst，第一个基于FCN的盒子和无NMS实例分段框架，实现竞争性能，例如，使用Resnet-50-FPN和40.2 mask AP使用Resnet-101-FPN，使用Resnet-50-FPN和40.2 mask AP，使用Resnet-101-FPN，对抗AP可可测试-DEV的主流方法。此外，提出的实例感知方法对于遮挡场景是可靠的，在重锁定的ochuman基准上，通过杰出的掩码AP优于公共基线。我们的代码将在出版后提供。

本文提出了一种用于对象和场景的高质量图像分割的新方法。灵感来自于形态学图像处理技术中的扩张和侵蚀操作，像素级图像分割问题被视为挤压对象边界。从这个角度来看，提出了一种新颖且有效的\ textBF {边界挤压}模块。该模块用于从内侧和外侧方向挤压对象边界，这有助于精确掩模表示。提出了双向基于流的翘曲过程来产生这种挤压特征表示，并且设计了两个特定的损耗信号以监控挤压过程。边界挤压模块可以通过构建一些现有方法构建作为即插即用模块，可以轻松应用于实例和语义分段任务。此外，所提出的模块是重量的，因此具有实际使用的潜力。实验结果表明，我们简单但有效的设计可以在几个不同的数据集中产生高质量的结果。此外，边界上的其他几个指标用于证明我们对以前的工作中的方法的有效性。我们的方法对实例和语义分割的具有利于Coco和CityCapes数据集来产生重大改进，并且在相同的设置下以前的最先进的速度优于先前的最先进的速度。代码和模型将在\ url {https:/github.com/lxtgh/bsseg}发布。

We present a new, embarrassingly simple approach to instance segmentation. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that have made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the "detect-then-segment" strategy (e.g., Mask R-CNN), or predict embedding vectors first then use clustering techniques to group pixels into individual instances. We view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size, thus nicely converting instance segmentation into a single-shot classification-solvable problem. We demonstrate a much simpler and flexible instance segmentation framework with strong performance, achieving on par accuracy with Mask R-CNN and outperforming recent single-shot instance segmenters in accuracy. We hope that this simple and strong framework can serve as a baseline for many instance-level recognition tasks besides instance segmentation. Code is available at https://git.io/AdelaiDet

Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance classification is used as mask quality score in most instance segmentation frameworks. However, the mask quality, quantified as the IoU between the instance mask and its ground truth, is usually not well correlated with classification score. In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN. We hope our simple and effective approach will provide a new direction for improving instance segmentation. The source code of our method is available at https:// github.com/zjhuang22/maskscoring_rcnn. * The work was done when Zhaojin Huang was an intern in Horizon Robotics Inc.

作为一个常见的图像编辑操作，图像组成旨在将前景从一个图像切割并粘贴在另一个图像上，从而产生复合图像。但是，有许多问题可能使复合图像不现实。这些问题可以总结为前景和背景之间的不一致，包括外观不一致（例如，不兼容的照明），几何不一致（例如不合理的大小）和语义不一致（例如，不匹配的语义上下文）。先前的作品将图像组成任务分为多个子任务，其中每个子任务在一个或多个问题上目标。具体而言，对象放置旨在为前景找到合理的比例，位置和形状。图像混合旨在解决前景和背景之间的不自然边界。图像协调旨在调整前景的照明统计数据。影子生成旨在为前景产生合理的阴影。通过将所有上述努力放在一起，我们可以获取现实的复合图像。据我们所知，以前没有关于图像组成的调查。在本文中，我们对图像组成的子任务进行了全面的调查。对于每个子任务，我们总结了传统方法，基于深度学习的方法，数据集和评估。我们还指出了每个子任务中现有方法的局限性以及整个图像组成任务的问题。图像组合的数据集和代码在https://github.com/bcmi/awesome-image-composition上进行了总结。

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as predicting contour of instance through instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on the challenging COCO dataset.For the first time, we show that the complexity of instance segmentation, in terms of both design and computation complexity, can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task. Code is available at: github.com/xieenze/PolarMask.

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part-A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part-A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data. Code is available at https://github.com/sshaoshuai/PointCloudDet3D.

由于字体，大小，颜色和方向的各种文本变化，任意形状的场景文本检测是一项具有挑战性的任务。大多数现有基于回归的方法求助于回归文本区域的口罩或轮廓点以建模文本实例。但是，回归完整的口罩需要高训练的复杂性，并且轮廓点不足以捕获高度弯曲的文本的细节。为了解决上述限制，我们提出了一个名为TextDCT的新颖的轻巧锚文本检测框架，该框架采用离散的余弦变换（DCT）将文本掩码编码为紧凑型向量。此外，考虑到金字塔层中训练样本不平衡的数量，我们仅采用单层头来进行自上而下的预测。为了建模单层头部的多尺度文本，我们通过将缩水文本区域视为正样本，并通过融合来介绍一个新颖的积极抽样策略，并通过融合来设计特征意识模块（FAM），以实现空间意识和规模的意识丰富的上下文信息并关注更重要的功能。此外，我们提出了一种分割的非量最大抑制（S-NMS）方法，该方法可以过滤低质量的掩模回归。在四个具有挑战性的数据集上进行了广泛的实验，这表明我们的TextDCT在准确性和效率上都获得了竞争性能。具体而言，TextDCT分别以每秒17.2帧（FPS）和F-measure的F-MEASIE达到85.1，而CTW1500和Total-Text数据集的F-Measure 84.9分别为15.1 fps。

现有的突出实例检测（SID）方法通常从像素级注释数据集中学习。在本文中，我们向SID问题提出了第一个弱监督的方法。虽然在一般显着性检测中考虑了弱监管，但它主要基于使用类标签进行对象本地化。然而，仅使用类标签来学习实例知识的显着性信息是不普遍的，因为标签可能不容易地分离具有高语义亲和力的显着实例。由于子化信息提供了对突出项的数量的即时判断，因此自然地与检测突出实例相关，并且可以帮助分离相同实例的不同部分的同一类别的单独实例。灵感来自这一观察，我们建议使用课程和镇展标签作为SID问题的弱监督。我们提出了一种具有三个分支的新型弱监管网络：显着性检测分支利用类一致性信息来定位候选物体;边界检测分支利用类差异信息来解除对象边界;和Firedroid检测分支，使用子化信息来检测SALICE实例质心。然后融合该互补信息以产生突出的实例图。为方便学习过程，我们进一步提出了一种渐进的培训方案，以减少标签噪声和模型中学到的相应噪声，通过往复式突出实例预测和模型刷新模型。我们广泛的评估表明，该方法对精心设计的基线方法进行了有利地竞争，这些方法适应了相关任务。

Recent state-of-the-art one-stage instance segmentation model SOLO divides the input image into a grid and directly predicts per grid cell object masks with fully-convolutional networks, yielding comparably good performance as traditional two-stage Mask R-CNN yet enjoying much simpler architecture and higher efficiency. We observe SOLO generates similar masks for an object at nearby grid cells, and these neighboring predictions can complement each other as some may better segment certain object part, most of which are however directly discarded by non-maximum-suppression. Motivated by the observed gap, we develop a novel learning-based aggregation method that improves upon SOLO by leveraging the rich neighboring information while maintaining the architectural efficiency. The resulting model is named SODAR. Unlike the original per grid cell object masks, SODAR is implicitly supervised to learn mask representations that encode geometric structure of nearby objects and complement adjacent representations with context. The aggregation method further includes two novel designs: 1) a mask interpolation mechanism that enables the model to generate much fewer mask representations by sharing neighboring representations among nearby grid cells, and thus saves computation and memory; 2) a deformable neighbour sampling mechanism that allows the model to adaptively adjust neighbor sampling locations thus gathering mask representations with more relevant context and achieving higher performance. SODAR significantly improves the instance segmentation performance, e.g., it outperforms a SOLO model with ResNet-101 backbone by 2.2 AP on COCO \texttt{test} set, with only about 3\% additional computation. We further show consistent performance gain with the SOLOv2 model.

我们提出了一种基于动态卷积的3D点云的实例分割方法。这使其能够在推断时适应变化的功能和对象尺度。这样做避免了一些自下而上的方法的陷阱，包括对超参数调整和启发式后处理管道的依赖，以弥补物体大小的不可避免的可变性，即使在单个场景中也是如此。通过收集具有相同语义类别并为几何质心进行仔细投票的均匀点，网络的表示能力大大提高了。然后通过几个简单的卷积层解码实例，其中参数是在输入上生成的。所提出的方法是无建议的，而是利用适应每个实例的空间和语义特征的卷积过程。建立在瓶颈层上的轻重量变压器使模型可以捕获远程依赖性，并具有有限的计算开销。结果是一种简单，高效且健壮的方法，可以在各种数据集上产生强大的性能：ScannETV2，S3DIS和Partnet。基于体素和点的体系结构的一致改进意味着提出的方法的有效性。代码可在以下网址找到：https：//git.io/dyco3d

In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention. This paper presents a novel single-shot instance segmentation approach, namely Box2Mask, which integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision. Specifically, both the input image and its deep features are employed to evolve the level-set curves implicitly, and a local consistency module based on a pixel affinity kernel is used to mine the local context and spatial relations. Two types of single-stage frameworks, i.e., CNN-based and transformer-based frameworks, are developed to empower the level-set evolution for box-supervised instance segmentation, and each framework consists of three essential components: instance-aware decoder, box-level matching assignment and level-set evolution. By minimizing the level-set energy function, the mask map of each instance can be iteratively optimized within its bounding box annotation. The experimental results on five challenging testbeds, covering general scenes, remote sensing, medical and scene text images, demonstrate the outstanding performance of our proposed Box2Mask approach for box-supervised instance segmentation. In particular, with the Swin-Transformer large backbone, our Box2Mask obtains 42.4% mask AP on COCO, which is on par with the recently developed fully mask-supervised methods. The code is available at: https://github.com/LiWentomng/boxlevelset.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.