由于经过验证的2D检测技术的适用性，大多数当前点云检测器都广泛采用了鸟类视图（BEV）。但是，现有方法通过简单地沿高度尺寸折叠的体素或点特征来获得BEV特征，从而导致3D空间信息的重丢失。为了减轻信息丢失，我们提出了一个基于多级特征降低降低策略的新颖点云检测网络，称为MDRNET。在MDRNET中，空间感知的维度降低（SDR）旨在在体素至BEV特征转换过程中动态关注对象的宝贵部分。此外，提出了多级空间残差（MSR），以融合BEV特征图中的多级空间信息。关于Nuscenes的广泛实验表明，该提出的方法的表现优于最新方法。该代码将在出版时提供。

本地图像功能匹配，旨在识别图像对的识别和相应的相似区域，是计算机视觉中的重要概念。大多数现有的图像匹配方法遵循一对一的分配原则，并采用共同最近的邻居来确保跨图像之间本地特征之间的独特对应关系。但是，来自不同条件的图像可能会容纳大规模变化或观点多样性，以便一对一的分配可能在密集匹配中导致模棱两可或丢失的表示形式。在本文中，我们介绍了一种新颖的无探测器本地特征匹配方法Adamatcher，该方法首先通过轻巧的特征交互模块与密集的特征相关联，并估算了配对图像的可见面积，然后执行贴片级多到 - 一个分配可以预测匹配建议，并最终根据一对一的完善模块进行完善。广泛的实验表明，Adamatcher的表现优于固体基线，并在许多下游任务上实现最先进的结果。此外，多对一分配和一对一的完善模块可以用作其他匹配方法（例如Superglue）的改进网络，以进一步提高其性能。代码将在出版时提供。

对手示例是一些可以扰乱深度神经网络的输出的一些特殊输入，以便在生产环境中产生有意的误差。用于产生对抗性示例的大多数方法需要梯度信息。甚至是与生成模型无关的普遍扰动依赖于梯度信息的一定程度。程序噪声对手示例是对普发的示例生成的一种新方法，它使用计算机图形噪声快速生成通用的对抗扰动，同时不依赖于梯度信息。结合对抗的防御训练，我们使用Perlin噪声训练神经网络以获得可以防御程序噪声对抗的模型。结合使用基于预先训练的模型的模型微调方法，我们获得更快的培训以及更高的准确性。我们的研究表明，程序噪声对抗性实例是可辩护的，但为什么程序噪声可以产生对抗性实例，以及如何防御可能在未来出现的其他过程噪声对抗性示例仍有待调查。

站点选择确定新商店的最佳位置，这对商业成功至关重要。特别是，具有多源城市数据的人工智能广泛应用使智能网站选择有前途。然而，现有的数据驱动方法严重依赖于特征工程，面临业务泛化问题和复杂的关系建模。为了摆脱困境，在这项工作中，我们从知识图（kg）借阅思想，并提出了一个知识驱动的网站选择模型，简称知识。具体而言，通过蒸馏的知识和富裕的语义在千克，我们首先用城市的关键要素和捕获的语义关系构建城市千克（Urbankg）。基于URBANKG，我们采用了用于语义表示的预训练技术，该技巧被馈送到站点决策的编码器 - 解码器结构中。通过多关键消息传递和基于关系的基于路径的关注机制，知识成功地揭示了各种业务和站点选择标准之间的关系。两个数据集的广泛实验表明，知识概率表现出具有效率和解释性的代表性基准。

在工业应用中，电动机的故障近一半是由于滚动元件轴承（REB）的退化引起的。因此，准确估算REB的剩余使用寿命（RUL）对于确保机械系统的可靠性和安全至关重要。为了应对这一挑战，基于模型的方法通常受到数学建模的复杂性的限制。另一方面，传统的数据驱动方法需要巨大的努力来提取降解功能并构建健康指数。在本文中，提出了一个新颖的在线数据驱动框架，以利用深度卷积神经网络（CNN）的采用来预测轴承的统治。更具体地说，训练轴承的原始振动首先是使用Hilbert-huang变换（HHT）处理的，并将新型的非线性降解指标构建为学习标签。然后使用CNN来识别提取的降解指示器和训练轴承振动之间的隐藏模式，这使得可以自动估计测试轴承的降解。最后，通过使用$ \ epsilon $ -Support向量回归模型来预测测试轴承的规定。与最先进的方法相比，提出的规则估计框架的出色性能通过实验结果证明。提出的CNN模型的一般性也通过转移到经历不同操作条件的轴承来验证。

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.