深度估计是3D重建的具有挑战性的任务，以提高环境意识的准确性感测。这项工作带来了一系列改进的新解决方案，与现有方法相比，增加了一系列改进，这增加了对深度图的定量和定性理解。最近，卷积神经网络（CNN）展示了估计单眼图象的深度图的非凡能力。然而，传统的CNN不支持拓扑结构，它们只能在具有确定尺寸和重量的常规图像区域上工作。另一方面，图形卷积网络（GCN）可以处理非欧几里德数据的卷积，并且它可以应用于拓扑结构内的不规则图像区域。因此，在这项工作中为了保护对象几何外观和分布，我们的目的是利用GCN进行自我监督的深度估计模型。我们的模型包括两个并行自动编码器网络：第一个是一个自动编码器，它取决于Reset-50，并从输入图像和多尺度GCN上提取功能以估计深度图。反过来，第二网络将用于基于Reset-18的两个连续帧之间估计自我运动矢量（即3D姿势）。估计的3D姿势和深度图都将用于构建目标图像。使用与光度，投影和平滑度相关的损耗函数的组合用于应对不良深度预测，并保持对象的不连续性。特别是，我们的方法提供了可比性和有前途的结果，在公共基准和Make3D数据集中的高预测精度为89％，与最先进的解决方案相比，培训参数的数量减少了40％。源代码在https://github.com/arminmasoumian/gcndepth.git上公开可用

倒置摆是一种非线性不平衡系统，需要使用电动机控制以实现稳定性和平衡。倒置摆用乐高构建，并使用乐高思维NXT，这是一种可编程机器人，能够完成许多不同的功能。在本文中，提出了倒置摆的初始设计，研究了与乐高思维NXT兼容的不同传感器的性能。此外，还研究了计算机视觉实现维持系统所需的稳定性的能力。倒置摆是一种传统推车，可以使用模糊逻辑控制器来控制，该模糊逻辑控制器为推车产生自调谐PID控制以继续前进。模糊逻辑和PID在Matlab和Simulink中模拟，并且在LabVIEW软件中开发了机器人的程序。

本文的目的是描述一种在实时反馈中检测滑动和接触力的方法。在这种新颖的方法中，戴维斯相机由于其快速处理速度和高分辨率而被用作视觉触觉传感器。在具有不同形状，尺寸，重量和材料的四个物体上进行两百实验，以比较Baxter机器人夹持器的精度和响应以避免滑动。通过使用力敏感电阻（FSR402）验证了先进的方法。使用Davis Camera捕获的事件通过特定算法处理，以向允许其检测滑动的Baxter Robot提供反馈。

通过使用立体声相机或3D摄像机估计深度图像，确定场景中的对象和来自2D图像的相机传感器之间的距离。深度估计的结果是相对距离，可用于计算实际上适用的绝对距离。然而，距离估计非常具有挑战性，使用2D单手套相机。本文介绍了深度学习框架，由两个深度网络组成，用于使用单个图像进行深度估计和对象检测。首先，使用您只有一次（yolov5）网络，检测和本地化场景中的对象。并行地，使用深度自动统计器网络计算估计的深度图像以检测相对距离。基于对象检测的基于对象的Yolo使用监督学习技术训练，又逆转，深度估计网络是自我监督的培训。呈现距离估计框架是在室外场景的真实图像上进行评估。所达到的结果表明，该框架具有前景，其含量为96％，RMSE为0.203的正确绝对距离。

The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 million water clusters. The use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces training time from a reported 2.7 days on 0.5M clusters to 1.2 hours on 2.7M clusters. Finetuning the pretrained model for downstream tasks of molecular dynamics and transfer to a different potential energy surface took only 8.3 hours and 28 minutes, respectively, on a single GPU.

我们研究了评估基于微分方程（DE）网络的鲁棒性的问题和挑战，以防止合成分布转移。我们提出了一种新颖而简单的精度度量，可用于评估固有的鲁棒性并验证数据集损坏模拟器。我们还提出了方法论建议，注定要评估神经des'的鲁棒性的许多面孔，并将其与它们的离散对应物进行了严格的比较。然后，我们使用此标准来评估廉价数据增强技术，以证明神经ODE的自然鲁棒性，以防止多个数据集中的模拟图像损坏。

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.