这项工作介绍了一个新颖的知识蒸馏框架，用于分类任务，其中可用并考虑到现有子类信息。在具有少量类或二进制检测的分类任务中，从教师到学生的信息量受到限制，从而限制了知识蒸馏的效用。通过利用类中可能的子类信息可以提高性能。为此，我们提出了所谓的子类知识蒸馏（SKD），这是将预测子类知识从老师转移到较小学生的过程。在老师的课堂逻辑中不存在的有意义的信息，而是在子类徽标中存在（例如，课堂内的相似之处）将通过SKD传达给学生，然后将提高学生的表现。从分析上，我们衡量教师可以通过SKD向学生提供多少额外信息，以证明我们工作的功效。开发的框架是在临床应用中评估的，即结直肠息肉分类。这是两个类别和每个类的许多子类的实际问题。在此应用程序中，使用临床医生提供的注释来根据注释标签的学习方式来定义子类。接受SKD框架训练的轻巧，低复杂的学生的F1得分为85.05％，提高了1.47％，比学生分别接受和没有常规知识蒸馏的学生获得了2.10％的收益。接受和没有SKD的学生之间的2.10％的F1得分差距可以通过额外的子类知识来解释，即，每个样本的额外的0.4656标签位可以在我们的实验中转移。

Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP setting. We provide consistency and finite sample bounds for the excess risk. We also propose a modification for the SGD algorithm where we leverage machine learning methods to smooth the stochastic gradients and improve empirical performance. We exemplify the algorithm in a setting of great interest nowadays: the Functional Linear Regression model. In this case we consider a synthetic data example and examples with a real data classification problem.

当大量机器人试图到达公共区域时，会发生拥堵，导致严重的延误。为了最大程度地减少机器人群体中的交通拥堵，必须以分散的方式使用交通控制算法。基于旨在最大化共同目标区域吞吐量的策略，我们使用人工潜在领域为机器人开发了两种新颖的算法，以避免障碍和导航。一种算法是通过创建一个队列到达目标区域的启发的（单队列以前-SQF），而另一个使机器人通过使用矢量字段（触摸和运行矢量字段-TRVF）使机器人触摸圆形区域的边界。 。我们进行了仿真实验，以表明所提出的算法受其启发的理论策略的吞吐量，并将两种新型算法与同一问题的最先进算法进行比较（PCC，EE和PCC-EE）。 SQF算法明显优于大量机器人或圆形目标区域半径较小的所有其他算法。另一方面，对于有限数量的机器人，TRVF仅比SQF更好，而对于众多机器人来说，TRVF仅优于PCC。但是，它使我们能够分析当思想从理论策略转移到混凝土算法时对吞吐量的潜在影响，该算法考虑了改变机器人之间的线性速度和距离。

场景理解是一个活跃的研究区域。商业深度传感器（如Kinect）在过去几年中启用了几个RGB-D数据集的发布，它在3D场景理解中产生了新的方法。最近，在Apple的iPad和iPhone中推出LIDAR传感器，可以在他们通常使用的设备上访问高质量的RGB-D数据。这在对计算机视觉社区以及应用程序开发人员来说，这是一个全新的时代。现场理解的基本研究与机器学习的进步一起可以影响人们的日常经历。然而，将这些现场改变为现实世界经验的理解方法需要额外的创新和发展。在本文中，我们介绍了Arkitscenes。它不仅是具有现在广泛可用深度传感器的第一个RGB-D数据集，而且是我们最好的知识，它也是了解数据发布的最大的室内场景。除了来自移动设备的原始和处理的数据之外，Arkitscenes还包括使用固定激光扫描仪捕获的高分辨率深度图，以及手动标记为家具的大型分类的3D定向边界盒。我们进一步分析了两个下游任务数据的有用性：3D对象检测和色彩引导深度上采样。我们展示了我们的数据集可以帮助推动现有最先进的方法的边界，并引入了更好代表真实情景的新挑战。

图表神经网络（GNN）基于故障诊断（FD）近年来收到了越来越多的关注，因为来自来自多个应用域的数据可以有利地表示为图。实际上，与传统的FD方法相比，这种特殊的代表性表格导致了卓越的性能。在本次审查中，给出了GNN，对故障诊断领域的潜在应用以及未来观点的简单介绍。首先，通过专注于它们的数据表示，即时间序列，图像和图形，回顾基于神经网络的FD方法。其次，引入了GNN的基本原则和主要架构，注意了图形卷积网络，图注意网络，图形样本和聚合，图形自动编码器和空间 - 时间图卷积网络。第三，通过详细实验验证基于GNN的最相关的故障诊断方法，结论是基于GNN的方法可以实现良好的故障诊断性能。最后，提供了讨论和未来的挑战。

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.