3D多对象跟踪（MOT）确保在连续动态检测过程中保持一致性，有利于自动驾驶中随后的运动计划和导航任务。但是，基于摄像头的方法在闭塞情况下受到影响，准确跟踪基于激光雷达的方法的对象的不规则运动可能是具有挑战性的。某些融合方法效果很好，但不认为在遮挡下出现外观特征的不可信问题。同时，错误检测问题也显着影响跟踪。因此，我们根据组合的外观运动优化（Camo-Mot）提出了一种新颖的相机融合3D MOT框架，该框架使用相机和激光镜数据，并大大减少了由遮挡和错误检测引起的跟踪故障。对于遮挡问题，我们是第一个提出遮挡头来有效地选择最佳对象外观的人，从而减少了闭塞的影响。为了减少错误检测在跟踪中的影响，我们根据置信得分设计一个运动成本矩阵，从而提高了3D空间中的定位和对象预测准确性。由于现有的多目标跟踪方法仅考虑一个类别，因此我们还建议建立多类损失，以在多类别场景中实现多目标跟踪。在Kitti和Nuscenes跟踪基准测试上进行了一系列验证实验。我们提出的方法在KITTI测试数据集上的所有多模式MOT方法中实现了最先进的性能和最低的身份开关（IDS）值（CAR为23，行人为137）。并且我们提出的方法在Nuscenes测试数据集上以75.3％的AMOTA进行了所有算法中的最新性能。

本文通过连续行动解决了非平稳环境和游戏中的政策学习。我们提出了一种无需重新格局样式的增强算法porl，而不是受到跟随规范化领导者（FTRL）和镜像下降（MD）更新的想法的启发，而不是经典的奖励最大化机制。我们证明，PORL具有最后的融合保证，这对于对抗和合作游戏很重要。实证研究表明，在控制任务的静态环境中，PORL的性能同样好，甚至比软crip-Critic（SAC）算法更好。在包括动态环境，对抗性训练和竞争性游戏在内的非机构环境中，PORL在更好的最终政策表现和更稳定的培训过程中都优于SAC。

如何设计用于人类运动识别的最佳可穿戴设备对于可靠，准确的人机合作至关重要。先前的作品主要是通过启发性制造可穿戴设备。取而代之的是，本文提出了一个学术问题：我们可以设计一种优化算法来优化可穿戴设备的制造，例如自动弄清最佳传感器布置吗？具体而言，这项工作着重于优化用于FMG臂章的示型传感器（FMG）传感器的放置，以应用手臂运动识别。首先，基于图理论，考虑传感器的信号和连接性，对臂章进行了建模。然后，引入了基于图形的臂章建模网络（GAM-NET），以供手臂运动识别。之后，制定了FMG臂章的传感器放置优化，并提出了具有贪婪的本地搜索的优化算法。为了研究我们的优化算法的有效性，收集了使用带有16个传感器的FMG臂章的机械维护任务的数据集。我们的实验表明，仅使用使用我们的算法优化的4个传感器可以帮助保持与使用所有传感器的可比识别精度。最后，从生理视图验证了优化的传感器放置结果。这项工作希望阐明考虑下游任务（例如人类生物信号收集和运动识别）的可穿戴设备的自动制造。我们的代码和数据集可从https://github.com/jerryx1110/iros22-fmg-sensor-optimization获得

本文的目标是对面部素描合成（FSS）问题进行全面的研究。然而，由于获得了手绘草图数据集的高成本，因此缺乏完整的基准，用于评估过去十年的FSS算法的开发。因此，我们首先向FSS引入高质量的数据集，名为FS2K，其中包括2,104个图像素描对，跨越三种类型的草图样式，图像背景，照明条件，肤色和面部属性。 FS2K与以前的FSS数据集不同于难度，多样性和可扩展性，因此应促进FSS研究的进展。其次，我们通过调查139种古典方法，包括34个手工特征的面部素描合成方法，37个一般的神经式传输方法，43个深映像到图像翻译方法，以及35个图像 - 素描方法。此外，我们详细说明了现有的19个尖端模型的综合实验。第三，我们为FSS提供了一个简单的基准，名为FSGAN。只有两个直截了当的组件，即面部感知屏蔽和风格矢量扩展，FSGAN将超越所提出的FS2K数据集的所有先前最先进模型的性能，通过大边距。最后，我们在过去几年中汲取的经验教训，并指出了几个未解决的挑战。我们的开源代码可在https://github.com/dengpingfan/fsgan中获得。

由于深入学习技术的快速进展和大型培训集的广泛可用性，视频显着性检测模型的性能一直在稳定地改善。然而，基于深度学习的VisualAudio固定预测仍处于起步阶段。目前，只提供了一些视觉音频序列，实际固定在真实的视觉音频环境中记录。因此，在相同的视觉音频环境下回忆真实固定，它既不有效也不是必要的。为了解决这个问题，本文以弱策略的方式促进一种新的方法，以减轻对视觉音频模型培训的大规模培训集的需求。仅使用视频类别标签，我们提出了选择性类激活映射（SCAM）及其升级（诈骗+）。在空间 - 时间 - 音频环境中，前者遵循粗致细的策略来选择最辨别的区域，并且这些区域通常能够与真正的人眼固定表现出高一致性。后者用额外的多粒度感知机制配备了骗局，使整个过程更加符合真正的人类视觉系统。此外，我们从这些区域蒸馏出知识，以获得完整的新空间 - 音频（STA）固定预测（FP）网络，在视频标签不可用的情况下实现广泛的应用。不借助任何真正的人眼固定，这些STA FP网络的性能与完全监督网络的性能相当。代码和结果在https://github.com/guotaowang/stanet上公开使用。

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.