由于其独立性与标签及其稳健性的独立性，自我监督的学习最近引起了很多关注。目前关于本主题的研究主要使用诸如图形结构的静态信息，但不能很好地捕获诸如边缘时间戳的动态信息。现实图形通常是动态的，这意味着节点之间的交互发生在特定时间。本文提出了一种自我监督的动态图形表示学习框架（DYSUBC），其定义了一个时间子图对比学学习任务，以同时学习动态图的结构和进化特征。具体地，首先提出了一种新的时间子图采样策略，其将动态图的每个节点作为中心节点提出，并使用邻域结构和边缘时间戳来采样相应的时间子图。然后根据在编码每个子图中的节点之后，根据中心节点上的邻域节点的影响设计子图表示功能。最后，定义了结构和时间对比损失，以最大化节点表示和时间子图表示之间的互信息。五个现实数据集的实验表明（1）DySubc比下游链路预测任务中的两个图形对比学习模型和四个动态图形表示学习模型更好地表现出更好的相关基线，（2）使用时间信息不能使用只有更有效的子图，还可以通过时间对比损失来学习更好的表示。

基于多利息框架的顺序推荐将用户最近的交互序列模拟到多个不同的兴趣向量中，因为单个低维向量不能完全代表用户兴趣的分集。然而，大多数现有模型只拦截用户最近的交互行为作为训练数据，丢弃大量的历史相互作用序列。这可能会提出两个问题。一方面，缺少反映用户多重兴趣的数据;另一方面，历史用户项交互中的项目之间的共同发生不会完全探索。为了解决这两个问题，本文提出了一种名为“全局交互感知多息框架的新型顺序推荐模型，用于顺序推荐（Gimirec）”。具体地，首先提出了一种全局上下文提取模块而不引入任何外部信息，该外部信息基于每个项目对的受约束的共生发生号码和它们的时间间隔从所有用户的历史交互序列的时间间隔计算加权共生发生矩阵通过使用简化的图形卷积获得每个项目的全局上下文嵌入。其次，捕获每个项目对最近的每个用户的交互序列的时间间隔并与全局上下文项嵌入以获取个性化项目嵌入的全局上下文项。最后，应用了一种基于自我关注的多息框架来学习用户对顺序推荐的不同兴趣。在亚马逊书籍的三个现实世界数据集上进行了广泛的实验，淘宝买和亚马逊 - 混合动力表明，Gimirec在召回，NDCG和命中率指标上的表现明显优于最先进的方法。此外，所提出的全局上下文提取模块可以很容易地移植到大多数顺序推荐模型。

随着人工智能和机器学习的日益普及，文献中已经提出了针对深度学习模型的广泛攻击。逃避攻击和中毒攻击都试图利用对抗性变化的样本来欺骗受害者模型以错误地分类对抗样本。尽管这种攻击声称是隐形的，即对人的眼睛看不见，但很少评估这种说法。在本文中，我们介绍了第一个大规模研究，涉及对深度学习的攻击中使用的对抗样本的隐身性。我们已经对六个流行的基准数据集实施了20种代表性的对抗ML攻击。我们使用两种互补方法评估了攻击样本的隐身性：（1）一项数值研究，采用24个指标用于图像相似性或质量评估； （2）对3组问卷的用户研究，从1,000多个回答中收集了20,000多次注释。我们的结果表明，大多数现有攻击引入了不可忽略的扰动，这些扰动对人的眼睛并不隐秘。我们进一步分析了有助于攻击隐身性的因素。我们进一步研究了数值分析与用户研究之间的相关性，并证明某些图像质量指标可能在攻击设计中提供有用的指导，而评估的图像质量和攻击的视觉隐身性之间仍然存在显着差距。

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.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.