Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. The source code of our benchmark is publicly available at https://github.com/fanxingliu2020/FedTADBench.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

由于其交易实体的伪匿名性质，比特币比任何其他金融资产都更频繁地进行非法活动。理想的检测模型有望实现（i）早期检测，（ii）良好的解释性和（iii）多功能性的所有三个特性。但是，现有的解决方案无法满足所有这些要求，因为它们中的大多数都在不满意的情况下严重依赖深度学习，并且仅用于对特定非法类型的回顾性分析。首先，我们提出资产转移路径，旨在描述解决早期特征。接下来，采用基于决策树的特征选择和分割策略，我们将整个观察期分为不同的段，并将每个段作为段向量进行编码。聚集了所有这些段向量后，我们获得了全局状态向量，本质上是描述整体意图的基本单元。最后，一个层次自我注意力预测指标可以实时预测给定地址的标签。生存模块告诉预测因子何时停止并提出状态序列，即意图。 ％依赖类型的选择策略和全球状态向量，我们的模型可用于检测具有强大解释性的各种非法活动。精心设计的预测指标和特定的损失功能可以进一步增强模型的预测速度和解释性。在三个现实世界数据集上进行的广泛实验表明，我们提出的算法优于最先进的方法。此外，其他案例研究证明我们的模型不仅可以解释现有的非法模式，还可以找到新的可疑字符。

Active域适应（ADA）查询所选目标样本的标签，以帮助将模型从相关的源域调整为目标域。由于其有希望的表现，标签成本最少，因此最近引起了人们越来越多的关注。然而，现有的ADA方法尚未完全利用查询数据的局部环境，这对ADA很重要，尤其是当域间隙较大时。在本文中，我们提出了一个局部环境感知的活动域适应性（LADA）的新框架，该框架由两个关键模块组成。本地上下文感知的活动选择（LAS）模块选择其类概率预测与邻居不一致的目标样本。局部上下文感知模型适应（LMA）模块完善了具有查询样本及其扩展的邻居的模型，并由上下文保留损失正规化。广泛的实验表明，与现有的主动选择策略相比，LAS选择了更多的信息样本。此外，配备了LMA，整个LADA方法的表现优于各种基准测试的最先进的ADA解决方案。代码可在https://github.com/tsun/lada上找到。

增强对未标记目标数据的模型预测置信度是无监督域适应（UDA）的重要目标。在本文中，我们探讨了关于倒数第二个线性分类层的输入特征的对抗性训练。我们表明，这种策略比以前的作品所使用的对对抗性图像或中间特征的对抗训练更有效，并且与提高预测置信度的目的更加相关。此外，通过在域适应中通常使用激活归一化以减少域间隙，我们得出了两个变体，并系统地分析了归一化对对抗性训练的影响。这在理论上和通过对实际适应任务的经验分析都进行了说明。在标准设置和无源DATA设置下，对流行的UDA基准测试进行了广泛的实验。结果证明了我们的方法可以在以前的艺术中取得最佳分数。

目标域中的标签放弃使无监督的域适应性（UDA）成为许多现实世界应用中的吸引力技术，尽管它也带来了巨大的挑战，因为没有标记目标数据，模型适应变得更加困难。在本文中，我们通过从目标领域的先验知识中寻求赔偿来解决这个问题，这在实践中通常（部分）可用于人类专业知识。这导致了一个新颖而实用的环境，除了训练数据外，还可以提供有关目标类别分布的一些先验知识。我们将该设置称为知识引导的无监督域适应性（KUDA）。特别是，我们考虑了有关目标域中类别分布的两种特定类型的先验知识：一个描述单个类概率的下层和上限的Unary Bound，以及描述了两个类概率之间关系的二进制关系。我们提出了一个使用此类先验知识来完善模型生成的伪标签的通用整流模块。该模块被配制为从先验知识和光滑的正常化程序中得出的零一编程问题。它可以很容易地插入基于自我训练的UDA方法中，我们将其与两种最先进的方法结合使用，即射击和用餐。四个基准测试的经验结果证实，整流模块显然改善了伪标签的质量，这反过来又受益于自我训练阶段。在先验知识的指导下，两种方法的性能都大大提高。我们希望我们的工作能够激发进一步的调查，以整合UDA的先验知识。代码可在https://github.com/tsun/kuda上找到。

包括传统浅层模型和深图神经网络（GNN）在内的图形嵌入方法已导致有希望的应用。然而，由于其优化范式，浅层模型尤其是基于随机步行的算法无法充分利用采样子图或序列中的邻居接近度。基于GNN的算法遇到了高阶信息的利用不足，在堆叠过多的层时很容易引起过度平滑的问题，这可能会恶化低度（长尾）项目的建议，从而限制了表现力和可伸缩性。在本文中，我们提出了一个新颖的框架SAC，即空间自动回归编码，以统一的方式解决上述问题。为了充分利用邻居接近和高级信息，我们设计了一种新型的空间自回旋范式。具体而言，我们首先随机掩盖了多跳的邻居，并通过以明确的多跳上注意来整合所有其他周围的邻居来嵌入目标节点。然后，我们加强模型，通过对比编码和蒙面邻居的嵌入来学习目标节点的邻居预测性编码，并配备了新的硬性阴性采样策略。为了了解目标到邻居预测任务的最小足够表示并删除邻居的冗余，我们通过最大化目标预测性编码和蒙面邻居的嵌入以及同时约束编码之间的相互信息来设计邻居信息瓶颈和周围的邻居的嵌入。公共推荐数据集和实际方案网络规模数据集Douyin-Friend-Recormendation的实验结果证明了SAC的优势与最先进的方法相比。

人员搜索旨在共同本地化和识别来自自然的查询人员，不可用的图像，这在过去几年中在计算机视觉社区中积极研究了这一图像。在本文中，我们将在全球和本地围绕目标人群的丰富的上下文信息中阐述，我们分别指的是场景和组上下文。与以前的作品单独处理这两种类型的作品，我们将它们利用统一的全球本地上下文网络（GLCNet），其具有直观的功能增强。具体地，以多级方式同时增强重新ID嵌入和上下文特征，最终导致人员搜索增强，辨别特征。我们对两个人搜索基准（即Cuhk-Sysu和PRW）进行实验，并将我们的方法扩展到更具有挑战性的环境（即，在MovieIenet上的字符搜索）。广泛的实验结果表明，在三个数据集上的最先进方法中提出的GLCNET的一致性改进。我们的源代码，预先训练的型号，以及字符搜索的新设置可以：https://github.com/zhengpeng7/llcnet。

显着对象检测（SOD）模拟了人类视觉感知系统以在场景中定位最具吸引力的对象，已广泛应用于各种计算机视觉任务。现在，随着深度传感器的出现，可以轻松捕获具有富裕的空间信息的深度图，并有利于提高SOD的性能。尽管在过去几年中提出了各种具有有前途的性能的基于RGB-D的SOD模型，但仍缺乏对这些主题的这些模型和挑战的深入了解。在本文中，我们从各个角度提供了基于RGB-D的SOD模型的全面调查，并详细介绍了相关的基准数据集。此外，考虑到光场还可以提供深度图，我们还从该域中回顾了SOD模型和流行的基准数据集。此外，为了研究现有模型的SOD能力，我们进行了全面的评估，以及基于属性的几种基于RGB-D的SOD模型的评估。最后，我们讨论了基于RGB-D的SOD的几个挑战和开放方向，以供未来的研究。将在https://github.com/taozh2017/rgbdsodsurvey上公开提供所有收集的模型，基准数据集，源代码链接，用于基于属性的评估的数据集以及评估代码

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.