由于全景分割为输入中的每个像素提供了一个预测,因此,非标准和看不见的对象系统地导致了错误的输出。但是,在关键的环境中,针对分发样本的鲁棒性和角案件对于避免危险行为至关重要,例如忽略动物或道路上的货物丢失。由于驾驶数据集不能包含足够的数据点来正确采样基础分布的长尾巴,因此方法必须处理未知和看不见的方案才能安全部署。以前的方法是通过重新识别已经看到未标记的对象来针对此问题的一部分。在这项工作中,我们扩大了提出整体分割的范围:一项任务,以识别和将看不见的对象分为实例,而无需从未知数中学习,同时执行已知类别的全面分割。我们用U3HS解决了这个新问题,U3HS首先将未知数视为高度不确定的区域,然后将相应的实例感知嵌入到各个对象中。通过这样做,这是第一次使用未知对象进行综合分割,我们的U3HS未接受未知数据的训练,因此使对象类型的设置不受限制,并允许对整体场景理解。在两个公共数据集上进行了广泛的实验和比较,即CityScapes和作为转移的丢失和发现,证明了U3HS在挑战性的整体分段任务中的有效性,并具有竞争性的封闭式全盘分段性能。
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The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.
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Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this work, we study the more general partial matching problem with multi-graph cycle consistency guarantees. Building on a recent progress in deep learning on graphs, we propose a novel data-driven method (URL) for partial multi-graph matching, which uses an object-to-universe formulation and learns latent representations of abstract universe points. The proposed approach advances the state of the art in semantic keypoint matching problem, evaluated on Pascal VOC, CUB, and Willow datasets. Moreover, the set of controlled experiments on a synthetic graph matching dataset demonstrates the scalability of our method to graphs with large number of nodes and its robustness to high partiality.
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The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning and computer vision are driving rapid progress in methods that can record the actions and interactions of multiple individuals simultaneously. Many social species, such as birds, however, live deeply embedded in a three-dimensional world. This world introduces additional perceptual challenges such as occlusions, orientation-dependent appearance, large variation in apparent size, and poor sensor coverage for 3D reconstruction, that are not encountered by applications studying animals that move and interact only on 2D planes. Here we introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers. Finally, we analyze captured ethogram data and demonstrate that social context affects the distribution of sequential interactions between birds in the aviary.
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Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain. We also investigate Active Learning for question answering in different stages, overall reducing the annotation effort of humans. For this purpose, we consider target domains in realistic settings, with an extremely low amount of annotated samples but with many unlabeled documents, which we assume can be obtained with little effort. Additionally, we assume sufficient amount of labeled data from the source domain is available. We perform extensive experiments to find the best setup for incorporating domain experts. Our findings show that our novel approach, where humans are incorporated as early as possible in the process, boosts performance in the low-resource, domain-specific setting, allowing for low-labeling-effort question answering systems in new, specialized domains. They further demonstrate how human annotation affects the performance of QA depending on the stage it is performed.
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The horseshoe prior is known to possess many desirable properties for Bayesian estimation of sparse parameter vectors, yet its density function lacks an analytic form. As such, it is challenging to find a closed-form solution for the posterior mode. Conventional horseshoe estimators use the posterior mean to estimate the parameters, but these estimates are not sparse. We propose a novel expectation-maximisation (EM) procedure for computing the MAP estimates of the parameters in the case of the standard linear model. A particular strength of our approach is that the M-step depends only on the form of the prior and it is independent of the form of the likelihood. We introduce several simple modifications of this EM procedure that allow for straightforward extension to generalised linear models. In experiments performed on simulated and real data, our approach performs comparable, or superior to, state-of-the-art sparse estimation methods in terms of statistical performance and computational cost.
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There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sustainable energy systems, pandemics, among others. In certain domains like chemistry, scientific discovery carries the extra burden of assessing risks of the proposed novel solutions before moving to the experimental stage. Despite several recent advances in Machine Learning and AI to address some of these challenges, there is still a gap in technologies to support end-to-end discovery applications, integrating the myriad of available technologies into a coherent, orchestrated, yet flexible discovery process. Such applications need to handle complex knowledge management at scale, enabling knowledge consumption and production in a timely and efficient way for subject matter experts (SMEs). Furthermore, the discovery of novel functional materials strongly relies on the development of exploration strategies in the chemical space. For instance, generative models have gained attention within the scientific community due to their ability to generate enormous volumes of novel molecules across material domains. These models exhibit extreme creativity that often translates in low viability of the generated candidates. In this work, we propose a workbench framework that aims at enabling the human-AI co-creation to reduce the time until the first discovery and the opportunity costs involved. This framework relies on a knowledge base with domain and process knowledge, and user-interaction components to acquire knowledge and advise the SMEs. Currently,the framework supports four main activities: generative modeling, dataset triage, molecule adjudication, and risk assessment.
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当考虑$ N $标记的机器人的运动计划时,我们需要通过一系列平行,连续的,无碰撞的机器人运动来重新布置给定的启动配置为所需的目标配置。目的是在最短的时间内达到新配置;一个重要的约束是始终保持群体连接。以前已经考虑过这种类型的问题,最近值得注意的结果可实现不一定连接的重新配置:如果将起始配置映射到目标配置,则需要最大的曼哈顿距离$ D $,则总体时间表的总持续时间可以是限制为$ \ Mathcal {O}(d)$,这是最佳选择的恒定因素。但是,只有在允许断开连接的重新配置或用于缩放的配置(通过将给定对象的所有维度通过相同的乘法因子增加到相同的乘法因子增加)时,才能实现恒定拉伸。我们通过(1)建立$ \ omega(\ sqrt {n})$的下限来解决这些主要的开放问题可以实现重新配置。此外,我们表明(3)决定是否可以实现2个制造物,而可以检查多项式时间是否可以实现1个制造pan。
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AASM准则是为了有一种常用的方法,旨在标准化睡眠评分程序的数十年努力的结果。该指南涵盖了从技术/数字规格(例如,推荐的EEG推导)到相应的详细睡眠评分规则到年龄的几个方面。在睡眠评分自动化的背景下,与许多其他技术相比,深度学习表现出更好的性能。通常,临床专业知识和官方准则对于支持自动睡眠评分算法在解决任务时至关重要。在本文中,我们表明,基于深度学习的睡眠评分算法可能不需要充分利用临床知识或严格遵循AASM准则。具体而言,我们证明了U-Sleep是一种最先进的睡眠评分算法,即使使用临床非申请或非规定派生,也可以解决得分任务,即使无需利用有关有关的信息,也无需利用有关有关的信息。受试者的年代年龄。我们最终加强了一个众所周知的发现,即使用来自多个数据中心的数据始终导致与单个队列上的培训相比,可以使性能更好。确实,我们表明,即使增加了单个数据队列的大小和异质性,后者仍然有效。在我们的所有实验中,我们使用了来自13个不同临床研究的28528多个多摄影研究研究。
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位置识别是可以协助同时定位和映射(SLAM)进行循环闭合检测和重新定位以进行长期导航的基本模块。在过去的20美元中,该地点认可社区取得了惊人的进步,这吸引了在计算机视觉和机器人技术等多个领域的广泛研究兴趣和应用。但是,在复杂的现实世界情景中,很少有方法显示出有希望的位置识别性能,在复杂的现实世界中,长期和大规模的外观变化通常会导致故障。此外,在最先进的方法之间缺乏集成框架,可以应对所有挑战,包括外观变化,观点差异,对未知区域的稳健性以及现实世界中的效率申请。在这项工作中,我们调查针对长期本地化并讨论未来方向和机会的最先进方法。首先,我们研究了长期自主权中的位置识别以及在现实环境中面临的主要挑战。然后,我们回顾了最新的作品,以应对各种位置识别挑战的不同传感器方式和当前的策略的认可。最后,我们回顾了现有的数据集以进行长期本地化,并为不同的方法介绍了我们的数据集和评估API。本文可以成为该地点识别界新手的研究人员以及关心长期机器人自主权的研究人员。我们还对机器人技术中的常见问题提供了意见:机器人是否需要准确的本地化来实现长期自治?这项工作以及我们的数据集和评估API的摘要可向机器人社区公开,网址为:https://github.com/metaslam/gprs。
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