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.

强大的深度学习技术的发展为社会和个人带来了一些负面影响。一个这样的问题是假媒体的出现。为了解决这个问题，我们组织了可信赖的媒体挑战（TMC）来探讨人工智能（AI）如何利用如何打击假媒体。我们与挑战一起发布了一个挑战数据集，由4,380张假和2,563个真实视频组成。所有这些视频都伴随着Audios，采用不同的视频和/或音频操作方法来生产不同类型的假媒体。数据集中的视频具有各种持续时间，背景，照明，最小分辨率为360p，并且可能包含模拟传输误差和不良压缩的扰动。我们还开展了用户学习，以展示所作数据集的质量。结果表明，我们的数据集具有有希望的质量，可以在许多情况下欺骗人类参与者。

职业性肺炎（OP）分期是有关受试者肺部健康的重要任务。患者的分期结果取决于分期标准和他的胸部X射线。它本质上是图像分类任务。但是，OP数据的分布通常是不平衡的，这在很大程度上降低了分类模型的效果，这些模型是在数据遵循平衡分布并导致分阶段不准确的假设下提出的。为了实现准确的操作分期，我们提出了一个能够在这项工作中处理不平衡数据的OP登台模型。所提出的模型采用灰度同时出现矩阵（GLCM）来提取胸部X射线的纹理特征，并使用加权宽学习系统（WBLS）实现分类。对医院提供的六个数据案例的实证研究表明，提出的模型可以比具有不平衡数据的最先进的分类器执行更好的OP分期。

潜在因子（LF）模型可有效地通过低级矩阵近似来表示高维和稀疏（HID）数据。Hessian无（HF）优化是利用LF模型目标函数的二阶信息的有效方法，并已用于优化二阶LF（SLF）模型。但是，SLF模型的低级表示能力在很大程度上取决于其多个超参数。确定这些超参数是耗时的，它在很大程度上降低了SLF模型的实用性。为了解决这个问题，在这项工作中提出了实用的SLF（PSLF）模型。它通过分布式粒子群优化器（DPSO）实现了超参数自加载，该粒子群（DPSO）无梯度且并行化。对真实HID数据集的实验表明，PSLF模型比在数据表示能力中的最先进模型具有竞争优势。

大规模的无向加权网络通常在与大数据相关的研究领域中发现。自然可以将其量化为用于实施大数据分析任务的对称高维和不完整（SHDI）矩阵。对称非负潜在因素分析（SNL）模型能够从SHDI基质中有效提取潜在因子（LFS）。然而，它依赖于约束培训计划，这使其缺乏灵活性。为了解决这个问题，本文提出了一个不受限制的对称非负潜在因素分析（USNL）模型。它的主要思想是两个方面：1）通过将非负映射函数集成到SNL模型中，输出LFS与决策参数分开； 2）随机梯度下降（SGD）用于实施不受限制的模型训练，并确保输出LFS非负性。对由实际的大数据应用产生的四个SHDI矩阵的实证研究表明，与SNL模型相比，USNL模型可实现缺失数据的预测准确性，以及高度竞争性的计算效率。

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.

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.