We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces. Our key idea is to incorporate segmentation probabilities as weights of a classical parameterization method, implemented as a novel differentiable parameterization layer within a neural network framework. We train a segmentation network to select 3D regions that are parameterized into 2D and penalized by the resulting distortion, giving rise to segmentations which are distortion-aware. Following training, a user can use our system to interactively select a point on the mesh and obtain a large, meaningful region around the selection which induces a low-distortion parameterization. Our code and project page are currently available.

本文介绍了一种数据驱动的形状完成方法，该方法着重于完成3D形状缺失区域的几何细节。我们观察到，现有的生成方法缺乏训练数据和表示能力，可以通过复杂的几何形状和拓扑合成合理的，细粒度的细节。我们的关键见解是从部分输入复制和变形补丁以完成缺失区域。这使我们能够保留本地几何特征的风格，即使它与培训数据有很大不同。我们的全自动方法分为两个阶段。首先，我们学会从输入形状检索候选补丁。其次，我们选择并变形了一些检索到的候选者，以无缝将它们融合到完整的形状中。该方法结合了两种最常见的完成方法的优点：基于相似性的单稳定性完成，以及通过学习形状空间来完成。我们通过从部分输入中检索贴片来利用重复模式，并通过使用神经网络来指导检索和变形步骤来学习全球结构先验。实验结果表明，我们的方法在多个数据集和形状类别上的表现非常优于基线。代码和数据可在https://github.com/gitbosun/patchrd上找到。

本文介绍了学习3D表面类似地图集的表示的新技术，即从2D域到表面的同质形态转换。与先前的工作相比，我们提出了两项​​主要贡献。首先，我们没有通过优化作为高斯人的混合物来了解具有任意拓扑的连续2D域，而不是将固定的2D域（例如一组平方斑）映射到表面上。其次，我们在两个方向上学习一致的映射：图表，从3D表面到2D域，以及参数化，它们的倒数。我们证明，这可以提高学到的表面表示的质量，并在相关形状集合中的一致性。因此，它导致了应用程序的改进，例如对应估计，纹理传输和一致的UV映射。作为额外的技术贡献，我们概述了，尽管合并正常的一致性具有明显的好处，但它会导致优化问题，并且可以使用简单的排斥正则化来缓解这些问题。我们证明我们的贡献比现有基线提供了更好的表面表示。

我们提出了一种从一系列时间演化点云序列中对时间一致的表面序列的无监督重建的方法。它在帧之间产生了密集和语义有意义的对应关系。我们将重建的表面代表由神经网络计算的Atlases，这使我们能够在帧之间建立对应关系。使这些对应关系的关键是语义上有意义的是为了保证在相应点计算的度量张量和尽可能相似。我们设计了一种优化策略，使我们的方法能够强大地对噪声和全局动作，而无需先验的对应关系或预先对准步骤。结果，我们的方法在几个具有挑战性的数据集中占据了最先进的。该代码可在https://github.com/bednarikjan/temporally_coherent_surface_reconstruction附近获得。

A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (i.e., to unseen data) properties concurrently. Existing techniques in federated learning (FL) have encountered a steep tradeoff between these objectives and impose large computational requirements on edge devices during training and inference. In this paper, we propose SplitGP, a new split learning solution that can simultaneously capture generalization and personalization capabilities for efficient inference across resource-constrained clients (e.g., mobile/IoT devices). Our key idea is to split the full ML model into client-side and server-side components, and impose different roles to them: the client-side model is trained to have strong personalization capability optimized to each client's main task, while the server-side model is trained to have strong generalization capability for handling all clients' out-of-distribution tasks. We analytically characterize the convergence behavior of SplitGP, revealing that all client models approach stationary points asymptotically. Further, we analyze the inference time in SplitGP and provide bounds for determining model split ratios. Experimental results show that SplitGP outperforms existing baselines by wide margins in inference time and test accuracy for varying amounts of out-of-distribution samples.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

NIR到VIS的面部识别是通过提取域不变特征来识别两个不同域的面。但是，由于两个不同的领域特征以及缺乏NIR FACE数据集，这是一个具有挑战性的问题。为了在使用现有面部识别模型时减少域差异，我们提出了一个“关系模块”，它可以简单地添加到任何面部识别模型中。从面部图像中提取的本地功能包含面部每个组件的信息。基于两个不同的域特征，使用本地特征之间的关系比以原样的方式使用它更具域名。除了这些关系外，位置信息，例如从嘴唇到下巴到眼睛到眼睛到眼睛的距离，还提供域不变的信息。在我们的关系模块中，关系层隐含地捕获关系，并协调层对位置信息进行建模。此外，我们提出的三重态损失和有条件的边缘损失减少了训练中类内部的变化，并导致了进一步的改进。与一般面部识别模型不同，我们的附加模块无需使用大型数据集进行预训练。所提出的模块仅使用CASIA NIR-VIS 2.0数据库进行微调。使用拟议的模块，我们达到了14.81％的排名1精度和15.47％的验证率，为0.1％的验证率与两个基线模型相比。

Majorana示威者是一项领先的实验，寻找具有高纯净锗探测器（HPGE）的中性s中性双β衰变。机器学习提供了一种最大化这些检测器提供的信息量的新方法，但是与传统分析相比，数据驱动的性质使其不可解释。一项可解释性研究揭示了机器的决策逻辑，使我们能够从机器中学习以反馈传统分析。在这项工作中，我们介绍了Majorana演示者数据的第一个机器学习分析。这也是对任何锗探测器实验的第一个可解释的机器学习分析。训练了两个梯度增强的决策树模型，以从数据中学习，并进行了基于游戏理论的模型可解释性研究，以了解分类功率的起源。通过从数据中学习，该分析识别重建参数之间的相关性，以进一步增强背景拒绝性能。通过从机器中学习，该分析揭示了新的背景类别对相互利用的标准Majorana分析的重要性。该模型与下一代锗探测器实验（如传说）高度兼容，因为它可以同时在大量探测器上进行训练。

我们从一组稀疏的光谱时间序列中构建了一个物理参数化的概率自动编码器（PAE），以学习IA型超新星（SNE IA）的内在多样性。 PAE是一个两阶段的生成模型，由自动编码器（AE）组成，该模型在使用归一化流（NF）训练后概率地解释。我们证明，PAE学习了一个低维的潜在空间，该空间可捕获人口内存在的非线性特征范围，并且可以直接从数据直接从数据中准确地对整个波长和观察时间进行精确模拟SNE IA的光谱演化。通过引入相关性惩罚项和多阶段训练设置以及我们的物理参数化网络，我们表明可以在训练期间分离内在和外在的可变性模式，从而消除了需要进行额外标准化的其他模型。然后，我们在SNE IA的许多下游任务中使用PAE进行越来越精确的宇宙学分析，包括自动检测SN Outliers，与数据分布一致的样本的产生以及在存在噪音和不完整数据的情况下解决逆问题限制宇宙距离测量。我们发现，与以前的研究相一致的最佳固有模型参数数量似乎是三个，并表明我们可以用$ 0.091 \ pm 0.010 $ mag标准化SNE IA的测试样本，该样本对应于$ 0.074 \ pm。 0.010 $ mag如果删除了特殊的速度贡献。训练有素的模型和代码在\ href {https://github.com/georgestein/supaernova} {github.com/georgestein/supaernova}上发布