无示例性课程学习（CIL）是一个具有挑战性的问题，因为严格禁止从先前阶段进行排练数据，从而导致灾难性忘记深度神经网络（DNNS）。在本文中，我们提出了ivoro，这是CIL的整体框架，源自计算几何形状。我们发现Voronoi图（VD）是一个用于空间细分的经典模型，对于解决CIL问题特别有力，因为VD本身可以以增量的方式构建好 - 新添加的站点（类）只会影响接近的类别，使非连续课程几乎无法忘记。此外，为了找到用于VD构建的更好的中心，我们使用功率图与VD串联DNN，并证明可以通过使用除法和争议算法集成本地DNN模型来优化VD结构。此外，我们的VD结构不限于深度特征空间，而是适用于多个中间特征空间，将VD推广为多中心VD（CIVD），可有效捕获DNN的多元元素特征。重要的是，Ivoro还能够处理不确定性感知的测试时间Voronoi细胞分配，并且在几何不确定性和预测精度之间表现出很高的相关性（高达〜0.9）。与最先进的非exemememplar CIL方法相比，Ivoro将所有内容汇总在一起，分别在CIFAR-100，Tinyimagenet和Imagenet-Subsset上获得了高达25.26％，37.09％和33.21％的改善。总之，Ivoro可以实现高度准确，保护隐私和几何解释的CIL，当禁止使用跨相数据共享时，这特别有用，例如在医疗应用中。我们的代码可在https://machunwei.github.io/ivoro上找到。

Safe and efficient co-planning of multiple robots in pedestrian participation environments is promising for applications. In this work, a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV). Also, we introduce K-step lookahead reward setting in multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with multi-head global attention module to better aggregates local observation information among different robots to guide the process of individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner.

卷积神经网络（CNN）通过深度体系结构获得了出色的性能。但是，这些CNN在复杂的场景下通常对图像超分辨率（SR）实现较差的鲁棒性。在本文中，我们通过利用不同类型的结构信息来获得高质量图像，提出了异质组SR CNN（HGSRCNN）。具体而言，HGSRCNN的每个异质组块（HGB）都采用含有对称组卷积块和互补的卷积块的异质体系结构，并以平行方式增强不同渠道的内部和外部关系，以促进富裕类型的较富裕类型的信息， 。为了防止出现获得的冗余功能，以串行方式具有信号增强功能的完善块旨在过滤无用的信息。为了防止原始信息的丢失，多级增强机制指导CNN获得对称架构，以促进HGSRCNN的表达能力。此外，开发了一种平行的向上采样机制来训练盲目的SR模型。广泛的实验表明，在定量和定性分析方面，提出的HGSRCNN获得了出色的SR性能。可以在https://github.com/hellloxiaotian/hgsrcnn上访问代码。

深卷积神经网络（CNN）用于图像通过自动挖掘精确的结构信息进行图像。但是，大多数现有的CNN依赖于扩大设计网络的深度以获得更好的降级性能，这可能会导致训练难度。在本文中，我们通过三个阶段（即动态卷积块（DCB），两个级联的小波变换和增强块（网络）和残留块（RB）（RB）（RB）（RB），提出了带有小波变换（MWDCNN）的多阶段图像。 。 DCB使用动态卷积来动态调整几次卷积的参数，以在降级性能和计算成本之间做出权衡。 Web使用信号处理技术（即小波转换）和判别性学习的组合来抑制噪声，以恢复图像Denoising中更详细的信息。为了进一步删除冗余功能，RB用于完善获得的功能，以改善通过改进残留密度架构来重建清洁图像的特征。实验结果表明，在定量和定性分析方面，提出的MWDCNN优于一些流行的非授权方法。代码可在https://github.com/hellloxiaotian/mwdcnn上找到。

具有强大学习能力的CNN被广泛选择以解决超分辨率问题。但是，CNN依靠更深的网络体系结构来提高图像超分辨率的性能，这可能会增加计算成本。在本文中，我们提出了一个增强的超分辨率组CNN（ESRGCNN），具有浅层架构，通过完全融合了深层和宽的通道特征，以在单图超级分辨率中的不同通道的相关性提取更准确的低频信息（ SISR）。同样，ESRGCNN中的信号增强操作对于继承更长途上下文信息以解决长期依赖性也很有用。将自适应上采样操作收集到CNN中，以获得具有不同大小的低分辨率图像的图像超分辨率模型。广泛的实验报告说，我们的ESRGCNN在SISR中的SISR性能，复杂性，执行速度，图像质量评估和SISR的视觉效果方面超过了最先进的实验。代码可在https://github.com/hellloxiaotian/esrgcnn上找到。

当前的全球本地化描述符通常在巨大的观点或外观变化下挣扎。一种可能的改进是添加有关语义对象的拓扑信息。然而，手工制作的拓扑描述符很难调节，并且对环境噪音，剧烈的透视变化，对象阻塞或错误进行错误而不强大。为了解决这个问题，我们通过将语义有意义的对象星座建模为图形，并使用深图卷积网络将星座映射到描述符来制定基于学习的方法。我们证明了我们深知的星座描述符（描述）在两个现实世界数据集上的有效性。尽管对随机生成的仿真数据集进行了描述培训，但它在现实世界数据集上显示出良好的概括能力。描述还优于最先进的和手工制作的星座描述符在全球本地化上，并且对不同类型的噪声非常有力。该代码可在https://github.com/ethz-asl/descriptellation上公开获得。

本文介绍了一种可以在非通信和局部可观察条件下应用的新型混合多机器人运动计划。策划员是无模型的，可以实现多机器人状态和观察信息的端到端映射到最终平滑和连续的轨迹。规划师是前端和后端分离的架构。前端协作航点搜索模块的设计基于具有分散执行图的集中培训下的多代理软演员批评算法。后端轨迹优化模块的设计基于具有安全区域约束的最小快照方法。该模块可以输出最终动态可行和可执行的轨迹。最后，多组实验结果验证了拟议的运动计划员的有效性。

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.