基于视频的人重新识别（REID）旨在识别多个非重叠摄像机的给定的行人视频序列。为了汇总视频样本的时间和空间特征，引入了图神经网络（GNN）。但是，现有的基于图的模型（例如STGCN）在节点功能上执行\ textIt {mean}/\ textit {max boming}以获取图表表示，该图表忽略了图形拓扑和节点的重要性。在本文中，我们建议图形池网络（GPNET）学习视频检索的多粒度图表示，其中实现了\ textit {Graph boming layer}，以简化图形。我们首先构建了一个多粒图，其节点特征表示由骨架学到的图像嵌入，并且在颞和欧几里得邻域节点之间建立了边缘。然后，我们实现多个图形卷积层以在图上执行邻域聚集。为了下图，我们提出了一个多头全注意图池（MHFAPOOL）层，该图集合了现有节点群集和节点选择池的优势。具体而言，MHFAPOOL将全部注意矩阵的主要特征向量作为聚合系数涉及每个汇总节点中的全局图信息。广泛的实验表明，我们的GPNET在四个广泛使用的数据集（即火星，dukemtmc-veneoreid，ilids-vid and Prid-2011）上实现了竞争结果。

现有的基于视频的人重新识别（REID）的方法主要通过功能提取器和功能聚合器来了解给定行人的外观特征。但是，当不同的行人外观相似时，外观模型将失败。考虑到不同的行人具有不同的步行姿势和身体比例，我们建议学习视频检索的外观功能之外的歧视性姿势功能。具体而言，我们实现了一个两分支的体系结构，以单独学习外观功能和姿势功能，然后将它们串联在一起进行推理。为了学习姿势特征，我们首先通过现成的姿势检测器检测到每个框架中的行人姿势，并使用姿势序列构建时间图。然后，我们利用复发图卷积网络（RGCN）来学习时间姿势图的节点嵌入，该姿势图设计了一种全局信息传播机制，以同时实现框内节点的邻域聚集，并在框架间图之间传递消息。最后，我们提出了一种由节点注意和时间注意的双重意见方法，以从节点嵌入中获得时间图表示，其中采用自我注意机制来了解每个节点和每个帧的重要性。我们在三个基于视频的REID数据集（即火星，Dukemtmc和Ilids-Vid）上验证了所提出的方法，其实验结果表明，学习的姿势功能可以有效地改善现有外观模型的性能。

基于模型的步态识别方法通常采用行人步行姿势来识别人类。但是，由于摄像头视图的改变，现有方法并未明确解决人类姿势的较大阶层差异。在本文中，我们建议通过通过低UPPER生成的对抗网络（Lugan）学习全级转换矩阵来为每个单视姿势样本生成多视图姿势序列。通过摄像机成像的先验，我们得出的是，跨视图之间的空间坐标满足了全级矩阵的线性转换，因此，本文采用了对抗性训练来从源姿势学习转换矩阵，并获得目标视图以获得目标。目标姿势序列。为此，我们实现了由图形卷积（GCN）层组成的发电机，完全连接（FC）层和两支分支卷积（CNN）层：GCN层和FC层编码源姿势序列和目标视图，然后是CNN分支最后，分别学习一个三角形基质和上三角基质，最后它们被乘以制定全级转换矩阵。出于对抗训练的目的，我们进一步设计了一个条件鉴别因子，该条件区分姿势序列是真实的还是产生的。为了启用高级相关性学习，我们提出了一个名为Multi尺度超图卷积（HGC）的插件播放模块，以替换基线中的空间图卷积层，该层可以同时模拟联合级别的部分，部分部分 - 水平和身体水平的相关性。在两个大型步态识别数据集（即CASIA-B和OUMVLP置位）上进行的广泛实验表明，我们的方法的表现优于基线模型，并以一个较大的边距基于基于姿势的方法。

在本文中，我们提出了一种用于HSI去噪的强大主成分分析的新型非耦合方法，其侧重于分别同时为低级和稀疏组分的等级和列方向稀疏性产生更准确的近似。特别是，新方法采用日志确定级别近似和新颖的$ \ ell_ {2，\ log} $常规，以便分别限制组件矩阵的本地低级或列明智地稀疏属性。对于$ \ ell_ {2，\ log} $ - 正常化的收缩问题，我们开发了一个高效的封闭式解决方案，该解决方案名为$ \ ell_ {2，\ log} $ - 收缩运算符。新的正则化和相应的操作员通常可以用于需要列明显稀疏性的其他问题。此外，我们在基于日志的非凸rpca模型中强加了空间光谱总变化正则化，这增强了从恢复的HSI中的空间和光谱视图中的全局转换平滑度和光谱一致性。关于模拟和实际HSIS的广泛实验证明了所提出的方法在去噪HSIS中的有效性。

图形神经网络（GNN）已被证明是分析非欧国人图数据的强大工具。但是，缺乏有效的分布图学习（GL）系统极大地阻碍了GNN的应用，尤其是当图形大且GNN相对深时。本文中，我们提出了GraphTheta，这是一种以顶点为中心的图形编程模型实现的新颖分布式和可扩展的GL系统。 GraphTheta是第一个基于分布式图处理的GL系统，其神经网络运算符以用户定义的功能实现。该系统支持多种培训策略，并在分布式（虚拟）机器上启用高度可扩展的大图学习。为了促进图形卷积实现，GraphTheta提出了一个名为NN-Tgar的新的GL抽象，以弥合图形处理和图形深度学习之间的差距。提出了分布式图引擎，以通过混合平行执行进行随机梯度下降优化。此外，除了全球批次和迷你批次外，我们还为新的集群批次培训策略提供了支持。我们使用许多网络大小的数据集评估GraphTheta，范围从小，适度到大规模。实验结果表明，GraphTheta可以很好地扩展到1,024名工人，用于培训内部开发的GNN，该工业尺度的Aripay数据集为14亿个节点和41亿个属性边缘，并带有CPU虚拟机（Dockers）群的小群。 （5 $ \ sim $ 12GB）。此外，GraphTheta比最先进的GNN实现获得了可比或更好的预测结果，证明其学习GNN和现有框架的能力，并且可以超过多达$ 2.02 \ tims $ $ 2.02 \ times $，具有更好的可扩展性。据我们所知，这项工作介绍了文献中最大的边缘属性GNN学习任务。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.