本文提出了一种新的点云卷积结构，该结构学习了SE（3） - 等级功能。与现有的SE（3） - 等级网络相比，我们的设计轻巧，简单且灵活，可以合并到一般的点云学习网络中。我们通过为特征地图选择一个非常规域，在模型的复杂性和容量之间取得平衡。我们通过正确离散$ \ mathbb {r}^3 $来完全利用旋转对称性来进一步减少计算负载。此外，我们采用置换层从其商空间中恢复完整的SE（3）组。实验表明，我们的方法在各种任务中实现了可比或卓越的性能，同时消耗的内存和运行速度要比现有工作更快。所提出的方法可以在基于点云的各种实用应用中促进模棱两可的特征学习，并激发现实世界应用的Equivariant特征学习的未来发展。

本文提出了一种可对应的点云旋转登记的方法。我们学习为每个点云嵌入保留所以（3）-equivariance属性的特征空间中的嵌入，通过最近的Quifariant神经网络的开发启用。所提出的形状登记方法通过用隐含形状模型结合等分性的特征学习来实现三个主要优点。首先，由于网络架构中类似于PointNet的网络体系结构中的置换不变性，因此删除了数据关联的必要性。其次，由于SO（3）的性能，可以使用喇叭的方法以闭合形式来解决特征空间中的注册。第三，由于注册和隐含形状重建的联合培训，注册对点云中的噪声强大。实验结果显示出优异的性能与现有的无对应的深层登记方法相比。

本文提出了一个统一的神经网络结构，用于联合3D对象检测和点云分段。我们利用检测和分割标签的丰富监督，而不是使用其中一个。另外，基于广泛应用于3D场景和对象理解的隐式功能，提出了基于单级对象检测器的扩展。扩展分支从对象检测模块作为输入采用最终特征映射，并产生隐式功能，为其对应的体素中心产生每个点的语义分布。我们展示了我们在NUSCENES-LIDARSEG上的结构的表现，这是一个大型户外数据集。我们的解决方案在与对象检测解决方案相比，在3D对象检测和点云分割中实现了针对现有的方法的竞争结果。通过实验验证了所提出的方法的有效弱监管语义分割的能力。

本文提出了一种从单个交通相机提取3D世界中车辆的位置和姿势的方法。从驾驶员的角度来看，大多数先前的单眼3D车辆检测算法集中在车辆上的摄像机上，并假定了已知的内在和外在校准。相反，本文侧重于使用未校准单眼交通摄像头的相同任务。我们观察到，道路平面和图像平面之间的相同特法对于3D车辆检测和该任务的数据合成至关重要，并且可以在没有相机内在和外部的情况下估计同字。通过在逆透视映射中产生的鸟瞰图（BEV）图像中估计旋转边界盒（R箱）进行3D车辆检测。我们提出了一个名为Daileed R-Box的新的回归目标和双视网架构，该架构促进了翘曲的BEV图像上的检测精度。实验表明，尽管在训练期间没有看到它们的成像，所提出的方法可以推广到新的相机和环境设置。

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised point-based neural renderer by comparing the rendered images with real images on massive RGB-D data. The learned point-cloud encoder can be easily integrated into various downstream tasks, including not only high-level tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image synthesis. Extensive experiments on various tasks demonstrate the superiority of our approach compared to existing pre-training methods.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.