在本文中，据我们所知，我们提供了将各种掩盖机制纳入变形金刚以可扩展方式融入变形金刚结构的第一种综合方法。我们表明，有关线性因果关注的最新结果（Choromanski等，2021）和对数线性RPE注意力（Luo等，2021）是这种一般机制的特殊情况。但是，通过将问题作为对未掩盖注意力的拓扑调制（基于图的）调制，我们以前获得了几个未知结果，包括有效的D维RPE掩盖和图形内掩蔽。我们利用许多数学技术，从光谱分析到动态编程和随机步行到新算法，以求解图形上的马尔可夫过程。我们提供相应的经验评估。

整个视频序列上的强大而准确的平面跟踪对于许多视觉应用至关重要。平面对象跟踪的关键是在参考图像和跟踪图像之间查找由同谱模型建模的对象对应关系。现有方法倾向于获得错误的对应关系，随着外观变化，相机对象相对运动和遮挡的变化。为了减轻这个问题，我们提出了一个统一的卷积神经网络（CNN）模型，该模型共同考虑了同谱，可见性和信心。首先，我们介绍了相关块，这些块明确地说明了本地外观变化，而相机对象相对运动是我们模型的基础。其次，我们共同学习将相机对象相对运动与遮挡联系起来的同谱和可见性。第三，我们提出了一个置信模块，该模块积极监视从相关块中获得的像素相关分布中的估计质量。所有这些模块都插入了Lucas-Kanade（LK）跟踪管道中，以获得准确且健壮的平面对象跟踪。我们的方法的表现优于公共锅和TMT数据集上的最新方法。在现实世界中，还验证了其出色的性能，从而综合了高质量的视频内广告。

3D面重建结果的评估通常取决于估计的3D模型和地面真相扫描之间的刚性形状比对。我们观察到，将两个形状与不同的参考点进行排列可以在很大程度上影响评估结果。这给精确诊断和改进3D面部重建方法带来了困难。在本文中，我们提出了一种新的评估方法，并采用了新的基准测试，包括100张全球对齐的面部扫描，具有准确的面部关键点，高质量的区域口罩和拓扑符合的网格。我们的方法执行区域形状比对，并导致计算形状误差期间更准确，双向对应关系。细粒度，区域评估结果为我们提供了有关最先进的3D面部重建方法表现的详细理解。例如，我们对基于单图像的重建方法的实验表明，DECA在鼻子区域表现最好，而Ganfit在脸颊区域的表现更好。此外，使用与我们构造的相同过程以对齐和重新构造几个3D面部数据集的新型和高质量的3DMM基础HIFI3D ++。我们将在https://realy3dface.com上发布真正的HIFI3D ++以及我们的新评估管道。

在NAS领域中，可分构造的架构搜索是普遍存在的，因为它的简单性和效率，其中两个范例，多路径算法和单路径方法主导。多路径框架（例如，DARTS）是直观的，但遭受内存使用和培训崩溃。单路径方法（例如，e.g.gdas和proxylesnnas）减轻了内存问题并缩小了搜索和评估之间的差距，但牺牲了性能。在本文中，我们提出了一种概念上简单的且有效的方法来桥接这两个范式，称为相互意识的子图可差架构搜索（MSG-DAS）。我们框架的核心是一个可分辨动的Gumbel-Topk采样器，它产生多个互斥的单路径子图。为了缓解多个子图形设置所带来的Severer Skip-Connect问题，我们提出了一个Dropblock-Identity模块来稳定优化。为了充分利用可用的型号（超级网和子图），我们介绍了一种记忆高效的超净指导蒸馏，以改善培训。所提出的框架击中了灵活的内存使用和搜索质量之间的平衡。我们展示了我们在想象中和CIFAR10上的方法的有效性，其中搜索的模型显示了与最近的方法相当的性能。

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.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.