从单眼RGB图像中重建3D手网络，由于其在AR/VR领域的巨大潜在应用，引起了人们的注意力越来越多。大多数最先进的方法试图以匿名方式解决此任务。具体而言，即使在连续录制会话中用户没有变化的实际应用程序中实际上可用，因此忽略了该主题的身份。在本文中，我们提出了一个身份感知的手网格估计模型，该模型可以结合由受试者的内在形状参数表示的身份信息。我们通过将提出的身份感知模型与匿名对待主题的基线进行比较来证明身份信息的重要性。此外，为了处理未见测试对象的用例，我们提出了一条新型的个性化管道来校准固有的形状参数，仅使用该受试者的少数未标记的RGB图像。在两个大型公共数据集上进行的实验验证了我们提出的方法的最先进性能。

最近，视觉变压器及其变体在人类和多视图人类姿势估计中均起着越来越重要的作用。将图像补丁视为令牌，变形金刚可以对整个图像中的全局依赖项进行建模或其他视图中的图像。但是，全球关注在计算上是昂贵的。结果，很难将这些基于变压器的方法扩展到高分辨率特征和许多视图。在本文中，我们提出了代币螺旋的姿势变压器（PPT）进行2D人姿势估计，该姿势估计可以找到粗糙的人掩模，并且只能在选定的令牌内进行自我注意。此外，我们将PPT扩展到多视图人类姿势估计。我们建立在PPT的基础上，提出了一种新的跨视图融合策略，称为人类区域融合，该策略将所有人类前景像素视为相应的候选者。可可和MPII的实验结果表明，我们的PPT可以在减少计算的同时匹配以前的姿势变压器方法的准确性。此外，对人类360万和滑雪姿势的实验表明，我们的多视图PPT可以有效地从多个视图中融合线索并获得新的最新结果。

估计每个视图中的2D人类姿势通常是校准多视图3D姿势估计的第一步。但是，2D姿势探测器的性能遭受挑战性的情况，例如闭塞和斜视角。为了解决这些挑战，以前的作品从eMipolar几何中的不同视图之间导出点对点对应关系，并利用对应关系来合并预测热插拔或特征表示。除了后预测合并/校准之外，我们引入了用于多视图3D姿势估计的变压器框架，其目的地通过将来自不同视图的信息集成信息来直接改善单个2D预测器。灵感来自先前的多模态变压器，我们设计一个统一的变压器体系结构，命名为输送，从当前视图和邻近视图中保险。此外，我们提出了eMipolar字段的概念来将3D位置信息编码到变压器模型中。由Epipolar字段引导的3D位置编码提供了一种有效的方式来编码不同视图的像素之间的对应关系。人类3.6M和滑雪姿势的实验表明，与其他融合方法相比，我们的方法更有效，并且具有一致的改进。具体而言，我们在256 x 256分辨率上只有5米参数达到人类3.6米的25.8毫米MPJPE。

尽管图表学习（GRL）取得了重大进展，但要以足够的方式提取和嵌入丰富的拓扑结构和特征信息仍然是一个挑战。大多数现有方法都集中在本地结构上，并且无法完全融合全球拓扑结构。为此，我们提出了一种新颖的结构保留图表学习（SPGRL）方法，以完全捕获图的结构信息。具体而言，为了减少原始图的不确定性和错误信息，我们通过k-nearest邻居方法构建了特征图作为互补视图。该特征图可用于对比节点级别以捕获本地关系。此外，我们通过最大化整个图形和特征嵌入的相互信息（MI）来保留全局拓扑结构信息，从理论上讲，该信息可以简化为交换功能的特征嵌入和原始图以重建本身。广泛的实验表明，我们的方法在半监督节点分类任务上具有相当出色的性能，并且在图形结构或节点特征上噪声扰动下的鲁棒性出色。

现有的少量学习（FSL）方法依赖于具有大型标记数据集的培训，从而阻止它们利用丰富的未标记数据。从信息理论的角度来看，我们提出了一种有效的无监督的FSL方法，并以自学意义进行学习表示。遵循信息原理，我们的方法通过捕获数据的内在结构来学习全面的表示。具体而言，我们以低偏置的MI估计量来最大化实例及其表示的相互信息（MI），以执行自我监督的预训练。我们的自我监督模型对所见类别的可区分特征的监督预训练没有针对可见的阶级的偏见，从而对看不见的类别进行了更好的概括。我们解释说，受监督的预训练和自我监督的预训练实际上正在最大化不同的MI目标。进一步进行了广泛的实验，以通过各种训练环境分析其FSL性能。令人惊讶的是，结果表明，在适当条件下，自我监管的预训练可以优于监督预训练。与最先进的FSL方法相比，我们的方法在没有基本类别的任何标签的情况下，在广泛使用的FSL基准上实现了可比的性能。

培训和评估之间的类别差距被特征为少量学习（FSL）成功的主要障碍之一。在本文中，我们首次凭证识别现实图像中的图像背景，作为课堂上的捷径知识，以适应课堂分类，而是超出FSL中的培训类别。一个小说框架COSOC，旨在通过在训练和评估中提取图像中的图像中的前景对象来解决这个问题而没有任何额外的监督。对电感FSL任务进行的广泛实验表明了我们方法的有效性。

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.