我们建议使用实例检测（实例检测）的新方法，合成优化的布局，以预处理对象检测器具有合成图像。我们的“固体”方法由两个主要组成部分组成：（1）使用具有优化场景布置的未标记的3D模型生成合成图像；（2）在“实例检测”任务上预修对象检测器 - 给定描绘对象的查询图像，检测目标图像中完全相同对象的所有实例。我们的方法不需要任何语义标签来进行预处理，并允许使用任意，不同的3D模型。对可可的实验表明，通过优化的数据生成和适当的预处理任务，合成数据可以是预处理对象探测器的高效数据。特别是，对渲染图像进行预修会在实际图像上预处理，同时使用明显较少的计算资源，从而实现了性能竞争。代码可在https://github.com/princeton-vl/solid上找到。

We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that Corner-Net achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.

Bio-inspired learning has been gaining popularity recently given that Backpropagation (BP) is not considered biologically plausible. Many algorithms have been proposed in the literature which are all more biologically plausible than BP. However, apart from overcoming the biological implausibility of BP, a strong motivation for using Bio-inspired algorithms remains lacking. In this study, we undertake a holistic comparison of BP vs. multiple Bio-inspired algorithms to answer the question of whether Bio-learning offers additional benefits over BP, rather than just biological plausibility. We test Bio-algorithms under different design choices such as access to only partial training data, resource constraints in terms of the number of training epochs, sparsification of the neural network parameters and addition of noise to input samples. Through these experiments, we notably find two key advantages of Bio-algorithms over BP. Firstly, Bio-algorithms perform much better than BP when the entire training dataset is not supplied. Four of the five Bio-algorithms tested outperform BP by upto 5% accuracy when only 20% of the training dataset is available. Secondly, even when the full dataset is available, Bio-algorithms learn much quicker and converge to a stable accuracy in far lesser training epochs than BP. Hebbian learning, specifically, is able to learn in just 5 epochs compared to around 100 epochs required by BP. These insights present practical reasons for utilising Bio-learning rather than just its biological plausibility and also point towards interesting new directions for future work on Bio-learning.

转置卷积在许多深度学习应用中都表现出突出。但是，由于在每个行和列中的每个元素之后添加零之后，特征映射的大小增加，因此转置卷积层在计算范围内都在计算密集型。因此，在扩展的输入特征图上进行的卷积操作导致硬件资源的利用率不佳。不必要的乘法操作的主要原因是在输入特征映射中的预定位置处的零。我们提出了一种算法级优化技术，用于有效的转置卷积实施以解决这些问题。基于内核激活，我们将原始内核隔离为四个子内核。该方案可以减少内存需求和不必要的乘法。我们提出的方法是使用Kaggle网站上的Flower DataSet使用Titan X GPU（Intel Dual Core CPU）的$ 3.09（3.02）\ Times $ $更快的计算。此外，提出的优化方法可以推广到现有设备，而无需其他硬件要求。一个简单的深度学习模型，其中包含一个转齿卷积层来评估优化方法。它显示出使用具有Intel双核CPU的MNIST数据集的$ 2.2 \ times $ $更快的培训。

最近的研究表明，X射线射线照相表现出比聚合酶链反应（PCR）检测更高的准确性。因此，将深度学习模型应用于X射线和放射线照相图像增加了确定COVID-19病例的速度和准确性。但是，由于健康保险的可移植性和问责制（HIPAA），医院由于隐私问题而不愿意共享患者数据。为了维持隐私，我们提出了不同的私人深度学习模型，以保护患者的私人信息。来自Kaggle网站的数据集用于评估用于COVID-19检测的设计模型。根据其最高测试精度选择了EditivedNet模型版本。将差异隐私约束注入到最佳模型中以评估性能。通过改变可训练的层，隐私损失以及每个样本中的限制信息来指出准确性。在微调过程中，我们获得了84 \％准确性，而隐私损失为10。

随着现代建筑倾向于使用大量玻璃面板，玻璃表面变得越来越无处不在。然而，这对机器人，自动驾驶汽车和无人机等自主系统的运营构成了重大挑战，因为玻璃板可能会成为导航的透明障碍。存在的工作试图利用各种线索，包括玻璃边界上下文或反思，例如先验。但是，它们都是基于输入RGB图像的。我们观察到3D深度传感器光线通过玻璃表面的传输通常会在深度图中产生空白区域，这可以提供其他见解以补充RGB图像特征以进行玻璃表面检测。在本文中，我们通过将RGB-D信息合并到两个新型模块中提出了一个新颖的玻璃表面检测框架：（1）一个跨模式环境挖掘（CCM）模块，以适应从RGB和深度学习个人和相互的上下文特征信息，以及（2）深度失误的注意力（DAA）模块，以明确利用空间位置，在这些空间位置存在缺失的深度以帮助检测玻璃表面的存在。此外，我们提出了一个大规模的RGB-D玻璃表面检测数据集，称为\ textit {RGB-D GSD}，用于RGB-D玻璃表面检测。我们的数据集包含3,009个现实世界的RGB-D玻璃表面图像，并具有精确的注释。广泛的实验结果表明，我们提出的模型优于最先进的方法。

随着社交媒体平台的可访问性迅速增加，有效的假新闻探测器变得至关重要。

联合学习（FL）作为边缘设备的有希望的技术，以协作学习共享预测模型，同时保持其训练数据，从而解耦了从需要存储云中的数据的机器学习的能力。然而，在规模和系统异质性方面，FL难以现实地实现。虽然有许多用于模拟FL算法的研究框架，但它们不支持在异构边缘设备上进行可扩展的流程。在本文中，我们呈现花 - 一种全面的FL框架，通过提供新的设施来执行大规模的FL实验并考虑丰富的异构流程来区分现有平台。我们的实验表明花卉可以仅使用一对高端GPU在客户尺寸下进行FL实验。然后，研究人员可以将实验无缝地迁移到真实设备中以检查设计空间的其他部分。我们认为花卉为社区提供了一个批判性的新工具，用于研究和发展。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning. However, during the training process, the transmission of model parameters can impose a significant load on the network bandwidth. It has been pointed out that the vast majority of model parameters are redundant during model parameter transmission. In this paper, we explore the data distribution law of selected partial model parameters on this basis, and propose a deep hierarchical quantization compression algorithm, which further compresses the model and reduces the network load brought by data transmission through the hierarchical quantization of model parameters. And we adopt a dynamic sampling strategy for the selection of clients to accelerate the convergence of the model. Experimental results on different public datasets demonstrate the effectiveness of our algorithm.