最近的端到端多对象检测器通过删除手工制作的过程（例如使用非最大最大抑制（NMS））删除手工制作的过程来简化推理管道。但是，在训练中，他们需要两分匹配来计算检测器输出的损失。与端到端学习的核心的方向性相反，双方匹配使端到端探测器复杂，启发式和依赖的培训。在本文中，我们提出了一种训练端到端多对象探测器而无需匹配的方法。为此，我们使用混合模型将端到端多对象检测作为密度估计问题。我们提出的检测器，称为稀疏混合物密度检测器（稀疏MDOD），使用混合模型估算边界盒的分布。稀疏MDOD是通过最大程度地减少负对数似然性和我们提出的正则化项，最大成分最大化（MCM）损失来训练的，从而阻止了重复的预测。在训练过程中，不需要其他过程，例如两分匹配，并且损失是直接从网络输出中计算出来的。此外，我们的稀疏MDOD优于MS-Coco上的现有检测器，MS-Coco是一种著名的多对象检测基准。

Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture. Code is available at https://github.com/jozhang97/DETA.

DETR方法中引入的查询机制正在改变对象检测的范例，最近有许多基于查询的方法获得了强对象检测性能。但是，当前基于查询的检测管道遇到了以下两个问题。首先，需要多阶段解码器来优化随机初始化的对象查询，从而产生较大的计算负担。其次，训练后的查询是固定的，导致不满意的概括能力。为了纠正上述问题，我们在较快的R-CNN框架中提出了通过查询生成网络预测的特征对象查询，并开发了一个功能性的查询R-CNN。可可数据集的广泛实验表明，我们的特征查询R-CNN获得了所有R-CNN探测器的最佳速度准确性权衡，包括最近的最新稀疏R-CNN检测器。该代码可在\ url {https://github.com/hustvl/featurized-queryrcnn}中获得。

对象检测是一项基本的计算机视觉任务，用于在给定图像中loccal和分类对象。大多数最先进的检测方法都利用固定数量的建议作为对象候选物的中间表示，在推理过程中无法适应不同的计算约束。在本文中，我们提出了一种简单而有效的方法，该方法通过生成动态提案以进行对象检测来适应不同的计算资源。我们首先设计一个模块来制作一个基于查询的模型，以便能够使用不同数量的建议进行推断。此外，我们将其扩展到动态模型，以根据输入图像选择建议数量，从而大大降低了计算成本。我们的方法在广泛的检测模型中实现了显着的加速，包括两阶段和基于查询的模型，同时获得相似甚至更好的准确性。

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。

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.

如果没有图像中的密集瓷砖锚点或网格点，稀疏的R-CNN可以通过以级联的训练方式更新的一组对象查询和建议框来实现有希望的结果。但是，由于性质稀疏以及查询与其参加地区之间的一对一关系，它在很大程度上取决于自我注意力，这通常在早期训练阶段不准确。此外，在密集对象的场景中，对象查询与许多无关的物体相互作用，从而降低了其独特性并损害了性能。本文提议在不同的框之间使用iOU作为自我注意力的价值路由的先验。原始注意力矩阵乘以从提案盒中计算出的相同大小的矩阵，并确定路由方案，以便可以抑制无关的功能。此外，为了准确提取分类和回归的功能，我们添加了两个轻巧投影头，以根据对象查询提供动态通道掩码，并且它们随动态convs的输出而繁殖，从而使结果适合两个不同的任务。我们在包括MS-Coco和CrowdHuman在内的不同数据集上验证了所提出的方案，这表明它可显着提高性能并提高模型收敛速度。

我们将Dino（\ textbf {d} etr与\ textbf {i} mpred de \ textbf {n} oising hand \ textbf {o} r boxes），一种最先进的端到端对象检测器。 ％ 在本文中。 Dino通过使用一种对比度方法来降级训练，一种用于锚定初始化的混合查询选择方法以及对盒子预测的两次方案，通过使用对比的方式来改善性能和效率的模型。 Dino在$ 12 $时代获得$ 49.4 $ ap，$ 12.3 $ ap in Coco $ 24 $时期，带有Resnet-50骨干和多尺度功能，可显着改善$ \ textbf {+6.0} $ \ textbf {ap}和ap {ap}和ap}和$ \ textbf {+2.7} $ \ textbf {ap}与以前的最佳detr样模型相比，分别是dn-detr。 Dino在模型大小和数据大小方面都很好地缩放。没有铃铛和哨子，在对objects365数据集进行了swinl骨架的预训练后，Dino在两个Coco \ texttt {val2017}（$ \ textbf {63.2} $ \ textbf {ap ap}）和\ testtt { -dev}（\ textbf {$ \ textbf {63.3} $ ap}）。与排行榜上的其他模型相比，Dino大大降低了其模型大小和预训练数据大小，同时实现了更好的结果。我们的代码将在\ url {https://github.com/ideacvr/dino}提供。

虽然用变压器（DETR）的检测越来越受欢迎，但其全球注意力建模需要极其长的培训期，以优化和实现有前途的检测性能。现有研究的替代方案主要开发先进的特征或嵌入设计来解决培训问题，指出，基于地区的兴趣区域（ROI）的检测细化可以很容易地帮助减轻DETR方法培训的难度。基于此，我们在本文中介绍了一种新型的经常性闪闪发光的解码器（Rego）。特别是，REGO采用多级复发处理结构，以帮助更准确地逐渐关注前景物体。在每个处理阶段，从ROI的闪烁特征提取视觉特征，其中来自上阶段的检测结果的放大边界框区域。然后，引入了基于一瞥的解码器，以提供基于前一级的瞥见特征和注意力建模输出的精细检测结果。在实践中，Refo可以很容易地嵌入代表性的DETR变体，同时保持其完全端到端的训练和推理管道。特别地，Refo帮助可变形的DETR在MSCOCO数据集上实现44.8AP，只有36个训练时期，与需要500和50时期的第一DETR和可变形的DETR相比，分别可以分别实现相当的性能。实验还表明，Rego始终如一地提升不同DETR探测器的性能高达7％的相对增益，在相同的50次训练时期。代码可通过https://github.com/zhechen/deformable-detr-rego获得。

Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS. In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective. Extensive experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well as its compatibility with and adaptivity to several state-of-the-art object detectors.

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN. To facilitate future research, two implementations are made available at https://github.com/zhaoweicai/cascade-rcnn (Caffe) and https://github.com/zhaoweicai/Detectron-Cascade-RCNN (Detectron).

DETR是使用变压器编码器 - 解码器架构的第一端到端对象检测器，并在高分辨率特征映射上展示竞争性能但低计算效率。随后的工作变形Detr，通过更换可变形的关注来提高DEDR的效率，这实现了10倍的收敛性和改进的性能。可变形DETR使用多尺度特征来改善性能，但是，与DETR相比，编码器令牌的数量增加了20倍，编码器注意的计算成本仍然是瓶颈。在我们的初步实验中，我们观察到，即使只更新了编码器令牌的一部分，检测性能也几乎没有恶化。灵感来自该观察，我们提出了稀疏的DETR，其仅选择性更新预期的解码器预期的令牌，从而有效地检测模型。此外，我们表明在编码器中的所选令牌上应用辅助检测丢失可以提高性能，同时最小化计算开销。即使在Coco数据集上只有10％的编码器令牌，我们验证稀疏DETR也可以比可变形DETR实现更好的性能。尽管只有编码器令牌稀疏，但总计算成本减少了38％，与可变形的Detr相比，每秒帧（FPS）增加42％。代码可在https://github.com/kakaobrain/sparse-dett

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn.

Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present RepPoints (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 AP 50 on the COCO test-dev detection benchmark, using ResNet-101 model. Code is available at https://github.com/microsoft/RepPoints.

我们为变体视觉任务提供了一个概念上简单，灵活和通用的视觉感知头，例如分类，对象检测，实例分割和姿势估计以及不同的框架，例如单阶段或两个阶段的管道。我们的方法有效地标识了图像中的对象，同时同时生成高质量的边界框或基于轮廓的分割掩码或一组关键点。该方法称为Unihead，将不同的视觉感知任务视为通过变压器编码器体系结构学习的可分配点。给定固定的空间坐标，Unihead将其自适应地分散到了不同的空间点和有关它们的关系的原因。它以多个点的形式直接输出最终预测集，使我们能够在具有相同头部设计的不同框架中执行不同的视觉任务。我们展示了对成像网分类的广泛评估以及可可套件的所有三个曲目，包括对象检测，实例分割和姿势估计。如果没有铃铛和口哨声，Unihead可以通过单个视觉头设计统一这些视觉任务，并与为每个任务开发的专家模型相比，实现可比的性能。我们希望我们的简单和通用的Unihead能够成为可靠的基线，并有助于促进通用的视觉感知研究。代码和型号可在https://github.com/sense-x/unihead上找到。

We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at:tinyurl.com/FCOSv1

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.

已经提出了各种模型来执行对象检测。但是，大多数人都需要许多手工设计的组件，例如锚和非最大抑制（NMS），以表现出良好的性能。为了减轻这些问题，建议了基于变压器的DETR及其变体可变形DETR。这些解决了为对象检测模型设计头部时的许多复杂问题。但是，当将基于变压器的模型视为其他模型的对象检测中的最新方法时，仍然存在对性能的疑问，这取决于锚定和NMS，揭示了更好的结果。此外，目前尚不清楚是否可以仅与注意模块结合使用端到端管道，因为Detr适应的变压器方法使用卷积神经网络（CNN）作为骨干身体。在这项研究中，我们建议将几个注意力模块与我们的新任务特异性分裂变压器（TSST）相结合是一种有力的方法，可以在没有传统手工设计的组件的情况下生成可可结果上最先进的性能。通过将通用注意模块分为两个分开的目标注意模块，该方法允许设计简单的对象检测模型。对可可基准的广泛实验证明了我们方法的有效性。代码可在https://github.com/navervision/tsst上获得

复杂的水下环境为物体检测带来了新的挑战，例如未平衡的光条件，低对比度，阻塞和水生生物的模仿。在这种情况下，水下相机捕获的物体将变得模糊，并且通用探测器通常会在这些模糊的物体上失败。这项工作旨在从两个角度解决问题：不确定性建模和艰难的例子采矿。我们提出了一个名为Boosting R-CNN的两阶段水下检测器，该检测器包括三个关键组件。首先，提出了一个名为RetinArpn的新区域建议网络，该网络提供了高质量的建议，并考虑了对象和IOU预测，以确定对象事先概率的不确定性。其次，引入了概率推理管道，以结合第一阶段的先验不确定性和第二阶段分类评分，以模拟最终检测分数。最后，我们提出了一种名为Boosting Reweighting的新的硬示例挖掘方法。具体而言，当区域提案网络误认为样品的对象的事先概率时，提高重新加权将在训练过程中增加R-CNN头部样品的分类损失，同时减少具有准确估计的先验的简易样品丢失。因此，可以在第二阶段获得强大的检测头。在推理阶段，R-CNN具有纠正第一阶段的误差以提高性能的能力。在两个水下数据集和两个通用对象检测数据集上进行的全面实验证明了我们方法的有效性和鲁棒性。