准确可靠的3D检测对于包括自动驾驶车辆和服务机器人在内的许多应用至关重要。在本文中，我们提出了一个具有点云序列的3D时间对象检测的灵活且高性能的3D检测框架，称为MPPNET。我们提出了一个新颖的三级结构框架，其中包含多帧特征编码和相互作用的代理点，以实现更好的检测。这三个层次结构分别进行每个帧的特征编码，短片特征融合和整个序列特征聚合。为了使用合理的计算资源来处理长期序列云，提出了组内特征混合和组间特征的注意，以形成第二和第三个特征编码层次结构，这些层次结构均经常应用于聚集多框架轨迹特征。代理不仅可以充当每个帧的一致对象表示，而且还充当了方便框架之间特征交互的快递。大型Waymo打开数据集的实验表明，当应用于短（例如4框架）和长（例如16框架）点云序列时，我们的方法优于具有较大边缘的最先进方法。代码可在https://github.com/open-mmlab/openpcdet上找到。

大多数现实世界情景的环境，如商场和超市始终变化。预构建的地图，不会占这些变化的内容容易过时。因此，有必要具有环境的最新模型，以促进机器人的长期运行。为此，本文呈现了一般终身同时定位和映射（SLAM）框架。我们的框架使用多个会话映射表示，并利用一个有效的地图更新策略，包括地图建筑，姿势图形细化和稀疏化。为了减轻内存使用情况的无限性增加，我们提出了一种基于Chow-Liu最大相互信息生成树的地图修剪方法。在真正的超市环境中，通过一个月的机器人部署全面验证了拟议的SLAM框架。此外，我们释放了从室内和户外变化环境中收集的数据集，希望加速在社区中的终身猛烈的Slam研究。我们的数据集可在https://github.com/sanduan168/lifelong-slam-dataset中获得。

本文提出了一种用于同时定位和映射（SLAM）系统的基于分层基于分段的优化方法。首先，我们提出了一种可靠的轨迹分割方法，可用于提高后端优化的效率。然后我们首次提出缓冲机制来提高分割的稳健性。在优化期间，我们使用全局信息来优化具有大错误的帧，而插值而不是优化，以更新估计估计的帧以根据每个帧的错误进行分级地分配计算量。基准测试的比较实验表明，我们的方法大大提高了优化效率，几乎没有准确性，并且通过大边距优于现有的高效优化方法。

在这里，我们提出了一种用于多模式神经影像融合学习（HGM）的异质图形神经网络。传统的基于GNN的模型通常假设大脑网络是具有单一类型节点和边缘的均匀图形。然而，巨大的文献已经显示出人脑的异质性，特别是在两个半球之间。均匀脑网络不足以模拟复杂的脑状态。因此，在这项工作中，我们首先用多型节点（即左右半球节点）和多型边缘（即半球形边缘）来模拟大脑网络作为异质图。此外，我们还提出了一种基于Hetergoneou Brain网络的自我监督的预训练策略，以解决由于复杂的模型和小样本大小而过度的问题。我们在两个数据集合的结果显示出拟议模型的优越性，以疾病预测任务的其他多模型方法。此外，消融实验表明，我们具有预训练策略的模型可以减轻训练样本大小有限的问题。

少量学习，特别是几秒钟的图像分类，近年来受到了越来越多的关注，并目睹了重大进展。最近的一些研究暗示表明，许多通用技术或“诀窍”，如数据增强，预训练，知识蒸馏和自我监督，可能大大提高了几次学习方法的性能。此外，不同的作品可以采用不同的软件平台，不同的训练计划，不同的骨干架构以及甚至不同的输入图像大小，使得公平的比较困难，从业者与再现性斗争。为了解决这些情况，通过在Pytorch中的同一单个代码库中重新实施17个最新的框架，提出了几次射门学习（Libfewshot）的全面图书馆。此外，基于libfewshot，我们提供多个基准数据集的全面评估，其中包含多个骨干架构，以评估不同培训技巧的常见缺陷和效果。此外，鉴于近期对必要性或未培训机制的必要性怀疑，我们的评估结果表明，特别是当与预训练相结合时，仍然需要这种机制。我们希望我们的工作不仅可以降低初学者的障碍，可以在几次学习上工作，而且还消除了非动力技巧的影响，促进了几枪学习的内在研究。源代码可从https://github.com/rl-vig/libfewshot获取。

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.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.