通过Perspective-N点（PNP）从单个RGB图像找到3D对象是计算机视觉中的长期问题。在端到端的深度学习的驱动下，最近的研究表明将PNP解释为一个可区分的层，因此可以通过反向传播梯度W.R.T.可以部分学习2d-3d点对应。对象姿势。然而，由于确定性姿势本质上是非差异的，因此学习整个不受限制的2D-3D点无法与现有方法融合。在本文中，我们提出了EPRO-PNP，这是用于一般端到端姿势估计的概率PNP层，该阶段估计输出了SE（3）歧管上的姿势分布，从本质上讲，将分类软效量带到连续域。 2d-3d坐标和相应的权重被视为通过最大程度地减少预测姿势分布和目标姿势分布之间的KL差异来学习的中间变量。基本原则统一了现有方法并类似于注意机制。 EPRO-PNP显着胜过竞争基线，缩小基于PNP的方法与LineMod 6DOF姿势估计和NUSCENES 3D对象检测基准的差距。

量子计算预计会对许多领域产生变革性的影响，但是其对行业问题的实际部署却没有得到充实的解放。我们专注于将量子计算应用于行业的运营管理问题，尤其是供应链管理。供应链管理中的许多问题都涉及大型州和行动空间，并在经典计算机上构成计算挑战。我们开发了一种量化的政策迭代算法来解决库存控制问题并证明其有效性。我们还深入讨论了在短期内实施该量子算法的硬件要求和潜在挑战。我们的模拟和实验由IBM Qiskit和Qbraid系统提供动力。

在许多环境中（来自人体肠道到海洋生态系统）的混合群落发现了生物体，并且可以对人类健康和环境产生深远的影响。 Metagenomics通过高通量测序研究这种群体的基因组材料，得到用于随后分析的DNA子序列。标准工作流程中称为啤酒的基本问题是发现与未知构成生物相关的基因组子组的群集。随后的固有噪声，需要对它们施加的各种生物限制以及偏斜簇大小分布加剧了这种无监督的学习问题的难度。在本文中，我们使用曲线图提出了一种新的配方，其中节点是子序列的，并且边缘代表同意信息。此外，我们模拟了提供了关于不能聚集在一起的节点的异细信号的生物限制。我们通过开发（i）图表示学习的新算法来解决融合问题，这些算法保留了奇妙关系和基于异语的基于约束的基于曲线的图形聚类方法，该方法解决了串簇大小分布的问题。在实际和合成数据集上的广泛实验证明我们的方法称为Repbin，优于各种各样的竞争方法。我们的约束图形表示学习和聚类方法，其在其他域中也可以是有用的，也可以推进距离偏心神经融合和图形表示学习的最先进。

巨大的数据分析变得越来越普遍，像BLB（小袋子袋）这样的数据采样方法用作评估大规模数据估算质量的强大工具。然而，对数据采样方法的性能受到调整参数的选择（例如，子集大小，每个子集的重建数）的影响。在本文中，我们开发了一个高参数选择方法，可用于选择用于子采样方法的调整参数。具体而言，通过仔细的理论分析，我们在各种子采样估算器的渐近效率与近双数目之间找到了分析简单而优雅的关系。这导致了普瑞达格的最佳选择。更具体地说，对于任意指定的超参数集，我们可以将其改进为一组新的超参数，没有额外的CPU时间成本，但结果估计的统计效率可以得到很大改善。仿真研究和实际数据分析都展示了我们方法的优势优势。

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.