能够直接在原始点云上学习有效的语义表示已成为3D理解中的一个核心主题。尽管进步迅速，但最新的编码器仍限制了典型的点云，并且在遇到几何变形扭曲时的性能弱于必要的性能。为了克服这一挑战，我们提出了Point-Stree，这是一种通用点云编码器，对基于放松的K-D树的转换非常可靠。我们方法的关键是使用主组件分析（PCA）在K-d树中设计了分区规则。我们将放松的K-D树的结构用作我们的计算图，并将特征作为边框描述符建模，并将其与点式最大最大操作合并。除了这种新颖的体系结构设计外，我们还通过引入预先对准进一步提高了鲁棒性 - 一种简单但有效的基于PCA的标准化方案。我们的PointTree编码器与预先对齐的结合始终优于大边距的最先进方法，用于从对象分类到广泛基础的数据集的各种转换版本的语义分割的应用程序。代码和预训练模型可在https://github.com/immortalco/pointtree上找到。

汉密尔顿蒙特卡洛（HMC）是抽样中的流行方法。尽管有很多关于各个方面的方法研究这种方法的作品，但一个有趣的问题是如何选择其集成时间以实现加速。在这项工作中，我们考虑通过HMC通过时间变化的集成时间来加速从分布$ \ pi（x）\ propto \ exp（-f（x））$采样的过程。当潜在的$ f $为$ l $ -smooth和$ m $ - $ -Strongly凸，即\ \ \用于从日志平滑且强烈的log-concove目标分配$ \ pi $进行采样时，已知在恒定的集成时间下，理想HMC需要获得$ \ epsilon $ wasserstein-2距离到目标$ \ pi $ is $ o（\ kappa \ log \ frac \ frac {1} {\ epsilon}）$的迭代数量kappa：= \ frac {l} {m} $是条件号。我们提出了一个基于Chebyshev多项式根源的时变整合时间的方案。我们表明，在二次潜在$ f $的情况下，即当目标$ \ pi $是高斯分布时，理想的HMC只需$ o（\ sqrt {\ kappa} \ log \ frac） {1} {\ epsilon}）$迭代数量到达Wasserstein-2距离小于$ \ epsilon $;对条件编号的依赖性的这种改善类似于优化的加速。 HMC随着建议的集成时间的设计和分析是建立在Chebyshev多项式工具上的。实验发现，即使是从没有二次的平稳凸电势的分布中进行的，即使是从具有平稳凸电势的分布中进行采样的优势也是如此。

如今，重球（HB）是非凸优化中最流行的动量方法之一。已经广泛观察到，将重球动态纳入基于梯度的方法中可以加速现代机器学习模型的训练过程。但是，建立其加速理论基础的进展显然远远落后于其经验成功。现有的可证明的加速结果是二次或近二次功能，因为当前显示HB加速度的技术仅限于Hessian固定时的情况。在这项工作中，我们开发了一些新技术，这些新技术有助于表现出二次超越二次的加速度，这是通过分析在两个连续时间点上如何变化的Hessian的变化来实现的，从而影响了收敛速度。基于我们的技术结果，一类Polyak- \ l {} Ojasiewicz（PL）优化问题可以通过HB确定可证明的加速度。此外，我们的分析证明了适应性设置动量参数的好处。

我们开发了一种使用无遗憾的游戏动态解决凸面优化问题的算法框架。通过转换最小化凸起函数以顺序方式解决Min-Max游戏的辅助问题的问题，我们可以考虑一系列必须在另一个之后选择其行动的两名员工的一系列策略。这些策略的常见选择是所谓的无悔的学习算法，我们描述了许多此类并证明了遗憾。然后，我们表明许多凸面优化的经典一阶方法 - 包括平均迭代梯度下降，弗兰克 - 沃尔夫算法，重球算法和Nesterov的加速方法 - 可以被解释为我们框架的特殊情况由于每个玩家都做出正确选择无悔的策略。证明该框架中的收敛速率变得非常简单，因为它们遵循适当已知的遗憾范围。我们的框架还引发了一些凸优化的特殊情况的许多新的一阶方法。

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.