公平的积极学习（FAL）利用积极的学习技术来实现有限的数据，并在敏感组之间达到公平性（例如，性别）。但是，FAL尚未解决对抗性攻击对各种安全至关重要的机器学习应用至关重要的影响。观察到这一点，我们介绍了一项新颖的任务，公平的健壮的积极学习（FRAL），整合了常规的FAL和对抗性鲁棒性。弗拉尔（Fral）要求ML模型利用主动学习技术在良性数据上共同实现均衡的绩效，并对群体之间的对抗性攻击进行均衡的鲁棒性。在这项新任务中，以前的FAL方法通常面临无法忍受的计算负担和无效性的问题。因此，我们通过联合不一致（JIN）制定了一种简单而有效的弗拉尔策略。为了有效地找到可以提高弱势组标签的性能和鲁棒性的样品，我们的方法利用了良性和对抗样本以及标准模型和强大模型之间的预测不一致。在不同的数据集和敏感组下进行的广泛实验表明，我们的方法不仅可以在良性样本上实现更公平的性能，而且与现有的活跃学习和FAL基本线相比，在白盒PGD攻击下，我们的方法还获得了更公平的鲁棒性。我们很乐观，弗拉尔将为开发安全，强大的ML研究和应用程序（例如生物识别系统中的面部属性识别）铺平道路。

恶意攻击者可以通过在图像上施加人类侵蚀的噪声来产生目标的对抗示例，从而迫使神经网络模型产生特定的不正确输出。通过跨模型可转移的对抗性示例，即使模型信息被攻击者保密，神经网络的脆弱性仍然存在。最近的研究表明，基于合奏的方法在生成可转移的对抗示例中的有效性。但是，在创建有针对性的攻击的情况下，现有方法缺乏在不同模型之间转移的目标攻击的情况。在这项工作中，我们提出了多样化的权重修剪（DWP），以通过利用在模型压缩中使用的权重修剪方法进一步增强基于合奏的方法。具体而言，我们通过随机的重量修剪方法获得多种不同的模型。这些模型可保留相似的精度，并可以作为基于合奏的方法的其他模型，从而产生更强的可转移目标攻击。在更具挑战性的情况下，提供了与Imagenet兼容数据集进行的实验：转移到不同的体系结构和对手训练的模型。结果表明，我们提出的DWP提高了目标攻击成功率，最先进方法的组合分别高达4.1％和8.0％

许多文献表明，基于及时的学习是使用大型预训练的语言模型的有效方法。最近的作品还展示了通过插入适当的提示来指导聊天机器人输出的可能性。基于梯度的方法通常用于扰动提示。但是，某些语言模型甚至无法为公众提供。在这项工作中，我们首先探讨了提示和加强学习（RL）与转向模型的生成的组合，而无需访问任何模型的参数。其次，为了减少培训工作并增强对看不见的任务的普遍性，我们应用多任务学习以使模型学会更好地对新任务进行推广。实验结果表明，我们提出的方法可以成功控制几个最新的（SOTA）对话模型，而无需访问其参数。此外，该模型证明了与基线模型更少的步骤快速适应看不见的任务的强大能力。

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.