Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.
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Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.
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Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block to decouple the classification and regression tasks. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.
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The upcoming large scale surveys like LSST are expected to find approximately $10^5$ strong gravitational lenses by analysing data of many orders of magnitude larger than those in contemporary astronomical surveys. In this case, non-automated techniques will be highly challenging and time-consuming, even if they are possible at all. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lenses. The advantages of self-attention-based encoder models over convolution neural networks are investigated, and ways to optimise the outcome of encoder models are analysed. We constructed and trained 21 self-attention based encoder models and five convolution neural networks to identify gravitational lenses from the Bologna Lens Challenge. Each model was trained separately using 18,000 simulated images, cross-validated using 2,000 images, and then applied to a test set with 100,000 images. We used four different metrics for evaluation: classification accuracy, area under the receiver operating characteristic curve (AUROC), the TPR$_0$ score and the TPR$_{10}$ score. The performances of self-attention-based encoder models and CNNs participating in the challenge are compared. They were able to surpass the CNN models that participated in the Bologna Lens Challenge by a high margin for the TPR$_0$ and TPR_${10}$. Self-Attention based models have clear advantages compared to simpler CNNs. They have highly competing performance in comparison to the currently used residual neural networks. Compared to CNNs, self-attention based models can identify highly confident lensing candidates and will be able to filter out potential candidates from real data. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNNs by acting as effective filters.
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DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10× less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at https:// github.com/fundamentalvision/Deformable-DETR.
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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}。
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在本文中,我们对检测变压器(DETR)感兴趣,这是一种基于变压器编码器编码器架构的端到端对象检测方法,而无需手工制作的后处理,例如NMS。受到有条件的Detr的启发,这是一种具有快速训练收敛性的改进的DETR,对内部解码器层提出了盒子查询(最初称为空间查询),我们将对象查询重新将对象查询重新布置为盒子查询的格式,该格式是参考参考嵌入的组成点和框相对于参考点的转换。该重新制定表明在更快地使用R-CNN中广泛研究的DETR中的对象查询与锚固框之间的联系。此外,我们从图像内容中学习了盒子查询,从而进一步提高了通过快速训练收敛的有条件DETR的检测质量。此外,我们采用轴向自我注意的想法来节省内存成本并加速编码器。所得的检测器(称为条件DETR V2)取得比条件DETR更好的结果,可节省内存成本并更有效地运行。例如,对于DC $ 5 $ -Resnet- $ 50 $骨干,我们的方法在可可$ Val $ set上获得了$ 44.8 $ ap,$ 16.4 $ fps和有条件的detr相比,它运行了$ 1.6 \ tims $ $ $ $ $,节省$ 74 $ \ \ \ \ \ \ \ \ \ \ \ \ \ $ 74美元总体内存成本的百分比,并提高$ 1.0 $ ap得分。
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人对象交互(HOI)检测作为对象检测任务的下游需要本地化人和对象,并从图像中提取人类和对象之间的语义关系。最近,由于其高效率,一步方法已成为这项任务的新趋势。然而,这些方法侧重于检测可能的交互点或过滤人对象对,忽略空间尺度处的不同物体的位置和大小的可变性。为了解决这个问题,我们提出了一种基于变压器的方法,Qahoi(用于人对象交互检测的查询锚点),它利用了多尺度架构来提取来自不同空间尺度的特征,并使用基于查询的锚来预测全部Hoi实例的元素。我们进一步调查了强大的骨干,显着提高了QAHOI的准确性,QAHOI与基于变压器的骨干优于最近的最近最先进的方法,通过HICO-DEC基准。源代码以$ \ href {https://github.com/cjw2021/qhoii} {\ text {this https url}} $。
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标记数据通常昂贵且耗时,特别是对于诸如对象检测和实例分割之类的任务,这需要对图像的密集标签进行密集的标签。虽然几张拍摄对象检测是关于培训小说中的模型(看不见的)对象类具有很少的数据,但它仍然需要在许多标记的基础(见)类的课程上进行训练。另一方面,自我监督的方法旨在从未标记数据学习的学习表示,该数据转移到诸如物体检测的下游任务。结合几次射击和自我监督的物体检测是一个有前途的研究方向。在本调查中,我们审查并表征了几次射击和自我监督对象检测的最新方法。然后,我们给我们的主要外卖,并讨论未来的研究方向。https://gabrielhuang.github.io/fsod-survey/的项目页面
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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.
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在本文中,我们提出了简单的关注机制,我们称之为箱子。它可以实现网格特征之间的空间交互,从感兴趣的框中采样,并提高变压器的学习能力,以获得几个视觉任务。具体而言,我们呈现拳击手,短暂的框变压器,通过从输入特征映射上的参考窗口预测其转换来参加一组框。通过考虑其网格结构,拳击手通过考虑其网格结构来计算这些框的注意力。值得注意的是,Boxer-2D自然有关于其注意模块内容信息的框信息的原因,使其适用于端到端实例检测和分段任务。通过在盒注意模块中旋转的旋转的不变性,Boxer-3D能够从用于3D端到端对象检测的鸟瞰图平面产生识别信息。我们的实验表明,拟议的拳击手-2D在Coco检测中实现了更好的结果,并且在Coco实例分割上具有良好的和高度优化的掩模R-CNN可比性。 Boxer-3D已经为Waymo开放的车辆类别提供了令人信服的性能,而无需任何特定的类优化。代码将被释放。
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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
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检测变压器已在富含样品的可可数据集上实现了竞争性能。但是,我们显示他们中的大多数人在小型数据集(例如CityScapes)上遭受了大量的性能下降。换句话说,检测变压器通常是渴望数据的。为了解决这个问题,我们通过逐步过渡从数据效率的RCNN变体到代表性的DETR,从经验中分析影响数据效率的因素。经验结果表明,来自本地图像区域的稀疏特征采样可容纳关键。基于此观察结果,我们通过简单地简单地交替如何在跨意义层构建键和价值序列,从而减少现有检测变压器的数据问题,并对原始模型进行最小的修改。此外,我们引入了一种简单而有效的标签增强方法,以提供更丰富的监督并提高数据效率。实验表明,我们的方法可以很容易地应用于不同的检测变压器,并在富含样品和样品的数据集上提高其性能。代码将在\ url {https://github.com/encounter1997/de-detrs}上公开提供。
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3D对象检测通过将点云作为唯一的输入来取得了显着的进展。但是,点云通常遭受不完整的几何结构和缺乏语义信息,这使得检测器难以准确地对检测到的对象进行分类。在这项工作中,我们专注于如何有效利用来自图像的对象级信息来提高基于点的3D检测器的性能。我们提出DEMF,这是一种简单而有效的方法,将图像信息融合到点特征中。给定一组点特征和图像特征图,DEMF通过将3D点的投影2D位置作为参考来自适应地汇总图像特征。我们在挑战性的Sun RGB-D数据集上评估了我们的方法,从而提高了最新的结果(+2.1 map@0.25和+2.3map@0.5)。代码可从https://github.com/haoy945/demf获得。
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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。
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3D视觉感知任务,包括基于多相机图像的3D检测和MAP分割,对于自主驾驶系统至关重要。在这项工作中,我们提出了一个称为BeVformer的新框架,该框架以时空变压器学习统一的BEV表示,以支持多个自主驾驶感知任务。简而言之,Bevormer通过通过预定义的网格形BEV查询与空间和时间空间进行交互来利用空间和时间信息。为了汇总空间信息,我们设计了空间交叉注意,每个BEV查询都从相机视图中从感兴趣的区域提取了空间特征。对于时间信息,我们提出暂时的自我注意力,以将历史bev信息偶尔融合。我们的方法在Nuscenes \ texttt {test} set上,以NDS度量为单位达到了新的最新56.9 \%,该设置比以前的最佳艺术高9.0分,并且与基于LIDAR的盆地的性能相当。我们进一步表明,BeVormer明显提高了速度估计的准确性和在低可见性条件下对象的回忆。该代码可在\ url {https://github.com/zhiqi-li/bevformer}中获得。
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基于查询的变压器在许多图像域任务中构建长期注意力方面表现出了巨大的潜力,但是由于点云数据的压倒性大小,在基于激光雷达的3D对象检测中很少考虑。在本文中,我们提出了CenterFormer,这是一个基于中心的变压器网络,用于3D对象检测。 CenterFormer首先使用中心热图在基于标准的Voxel点云编码器之上选择中心候选者。然后,它将中心候选者的功能用作变压器中的查询嵌入。为了进一步从多个帧中汇总功能,我们通过交叉注意设计一种方法来融合功能。最后,添加回归头以预测输出中心功能表示形式上的边界框。我们的设计降低了变压器结构的收敛难度和计算复杂性。结果表明,与无锚对象检测网络的强基线相比,有了显着改善。 CenterFormer在Waymo Open数据集上实现了单个模型的最新性能,验证集的MAPH为73.7%,测试集的MAPH上有75.6%的MAPH,大大优于所有先前发布的CNN和基于变压器的方法。我们的代码可在https://github.com/tusimple/centerformer上公开获取
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我们提出Osformer,这是伪装实例分割(CIS)的第一个单阶段变压器框架。Osformer基于两个关键设计。首先,我们设计了一个位置传感变压器(LST),以通过引入位置引导查询和混合通风volvolution feedforward网络来获得位置标签和实例感知参数。其次,我们开发了一个粗到细节的融合(CFF),以合并LST编码器和CNN骨架的各种上下文信息。结合这两个组件使Osformer能够有效地融合本地特征和远程上下文依赖关系,以预测伪装的实例。与两阶段的框架相比,我们的OSFORMER达到41%的AP并达到良好的收敛效率,而无需大量的训练数据,即仅3040个以下的样本以下60个时代。代码链接:https://github.com/pjlallen/osformer。
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Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search re-
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我们采用自我监督的代表性学习来从深色能源仪器遗产成像调查的数据释放9中从7600万个星系图像中提取信息9.针对新的强力引力镜头候选者的识别,我们首先创建了快速的相似性搜索工具,以发现新的搜索工具强镜仅给出一个单个标记的示例。然后,我们展示如何在自我监督的表示上训练简单的线性分类器,仅需几分钟即可在CPU上进行几分钟,可以自动以极高的效率对强镜进行分类。我们提出了1192个新的强镜候选者,我们通过简短的视觉标识活动确定,并释放一种基于Web的相似性搜索工具和顶级网络预测,以促进众包快速发现额外的强力镜头和其他稀有物体:HTTPS:https://github.com/georgestein/ssl-legacysurvey。
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