使用和部署不同本地模型的个性化联合学习（PFL），由于其在处理佛罗里达州客户的统计异质性方面的成功，近年来引起了人们的关注。但是，对不同PFL方法的标准化评估和系统分析仍然是一个挑战。首先，高度多样化的数据集，FL仿真设置和PFL实现可以防止对PFL方法的快速和公平比较。其次，在各种实践场景中，PFL方法的有效性和鲁棒性不足，例如新客户的概括和资源有限的客户参与。最后，当前的PFL文献在采用的评估和消融方案中有所不同。为了应对这些挑战，我们提出了第一个全面的PFL基准PFL基准，以促进快速，可重现，标准化和彻底的PFL评估。所提出的基准测试包含具有统一数据分区和现实异质设置的不同应用程序域中的10多个数据集；一个模块化且易于扩展的PFL代码库，具有20多个竞争性PFL基线实现；以及在集装环境下进行的系统评估，以概括，公平，系统开销和收敛性。我们强调了最先进的PFL方法的好处和潜力，并希望PFL板台实现了进一步的PFL研究和广泛的应用，否则由于缺乏专用的基准，这将是困难的。该代码在https://github.com/alibaba/federatedscope/tree/master/master/benchmark/pfl-bench上发布。

为了调查现实世界中联邦学习的异质性，我们将经典的联合学习概括为联合的异性任务学习，这强调了参与者在数据分布和学习任务方面的联盟学习中的不一致性。我们还提出了B-FHTL，这是一种联合的杂项任务学习基准，该基准包括模拟数据集，FL协议和统一的评估机制。 B-FHTL数据集包含三个精心设计的联合学习任务，异质性增加。每个任务都使用不同的非IID数据和学习任务模拟客户端。为了确保不同的FL算法之间的公平比较，B-FHTL通过提供高级API来避免隐私泄漏，在整个FL协议中构建，并预设跨越不同的学习任务的最常见评估指标，例如回归，分类，文本，文本，文本此外，我们还比较了B-FHTL中联合多任务学习，联合个性化和联合元学习领域的FL算法，并突出了联盟异质任务学习的异质性和困难的影响。我们的基准测试，包括联合数据集，协议，评估机制和初步实验，可在https://github.com/alibaba/federatedscope/tree/master/master/master/benchmark/b-fhtl上开放。

尽管现有联合学习平台（FL）平台已取得了显着的进展，以提供开发基础架构，但这些平台可能无法很好地应对各种异质性带来的挑战，包括参与者本地数据，资源，行为和学习目标中的异质性。为了填补这一空白，在本文中，我们提出了一个名为FederatedScope的新型FL平台，该平台采用事件驱动的架构为用户提供极大的灵活性，以独立描述不同参与者的行为。这样的设计使用户可以轻松地描述参与者具有各种本地培训过程，学习目标和后端，并通过同步或异步培训策略将其协调为FL课程。 FederatedScope为易于使用和灵活的平台提供了丰富类型的插入操作和组件，以有效地进行进一步开发，并且我们实施了几个重要组件，以更好地帮助用户进行隐私保护，攻击模拟和自动调整。我们已经在https://github.com/alibaba/federatedscope上发布了FederatedScope，以在各种情况下促进联邦学习的学术研究和工业部署。

由于COVID-19，许多学校通过视频会议软件在线考试已经采用了许多学校。虽然方便，但教师要同时显示的学生变焦窗口监督在线考试是具有挑战性的。在本文中，我们提出了IEXAM，这是一种智能的在线考试监测和分析系统，不仅可以使用面部检测来帮助监护人实时学生识别，而且还可以检测到常见的异常行为（包括面部消失，旋转的面部，旋转的面部，旋转，，旋转，并在考试期间用另一个人替换）通过基于面部识别后的外观后视频分析。为了建立这样的新型系统，我们克服了三个挑战。首先，我们发现了一种轻巧的方法来捕获考试视频流并实时分析它们。其次，我们利用每个学生的变焦窗口上显示的左角名称，并提出了改进的OCR（光学角色识别）技术来自动收集具有动态位置的学生面孔的地面真相。第三，我们进行了几次实验比较和优化，以有效缩短教师PC所需的训练时间和测试时间。我们的评估表明，IEXAM可以实现高精度，实时面部检测为90.4％，后验后面部识别率为98.4％，同时保持可接受的运行时性能。我们已经在https://github.com/vprlab/iexam上提供了IEXAM的源代码。

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.