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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随着点云上的3D对象检测依赖于点之间的几何关系,非标准对象形状可以妨碍方法的检测能力。然而,在安全关键环境中,在分销外和长尾样品上的鲁棒性是对规避危险问题的基础,例如损坏或稀有汽车的误读。在这项工作中,我们通过在训练期间考虑到变形的点云来大大改善3D对象探测器的概括到域名数据。我们通过3D-VFIEL实现这一点:一种新的方法,可以通过越野时代的载体衡量物体。我们的方法将3D点限制以沿着传感器视图幻灯片幻灯片,而既不添加也不添加它们中的任何一个。所获得的载体是可转移的,独立于样的和保持形状平滑度和闭塞。通过在训练期间使用这些载体场产生的变形来增强正常样本,我们显着改善了对不同形状物体的鲁棒性,例如损坏/变形汽车,即使仅在基蒂训练。为此,我们提出并分享开源Crashd:现实损坏和稀有汽车的合成数据集,具有各种碰撞情景。在Kitti,Waymo,我们的Crashd和Sun RGB-D上进行了广泛的实验,表明了我们对室内和室外场景的域外数据,不同型号和传感器,即LIDAR和TOF相机的技术的高度普遍性。我们的crashd数据集可在https://crashd-cars.github.io上获得。
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估计神经网络的不确定性在安全关键环境中起着基本作用。在对自主驾驶的感知中,测量不确定性意味着向下游任务提供额外的校准信息,例如路径规划,可以将其用于安全导航。在这项工作中,我们提出了一种用于对象检测的新型采样的不确定性估计方法。我们称之为特定网络,它是第一个为每个输出信号提供单独的不确定性:Objectness,类,位置和大小。为实现这一点,我们提出了一种不确定性感知的热图,并利用检测器提供的相邻边界框在推理时间。我们分别评估了不同不确定性估计的检测性能和质量,也具有具有挑战性的域名样本:BDD100K和肾上腺素训练在基蒂培训。此外,我们提出了一种新的指标来评估位置和大小的不确定性。当转移到看不见的数据集时,某些基本上概括了比以前的方法和集合更好,同时是实时和提供高质量和全面的不确定性估计。
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虽然在驾驶场景中自我监督的单眼深度估计已经取得了可比性的性能,但违反了静态世界假设的行为仍然可以导致交通参与者的错误深度预测,造成潜在的安全问题。在本文中,我们呈现R4DYN,这是一种新颖的技术,用于在自我监督深度估计框架之上使用成本高效的雷达数据。特别是,我们展示如何在培训期间使用雷达,以及额外的输入,以增强推理时间的估计稳健性。由于汽车雷达很容易获得,这允许从各种现有车辆中收集培训数据。此外,通过过滤和扩展信号以使其与基于学习的方法兼容,我们地满地雷达固有问题,例如噪声和稀疏性。通过R4DYN,我们能够克服自我监督深度估计的一个主要限制,即交通参与者的预测。我们大大提高了动态物体的估计,例如汽车在挑战的NUSCENES数据集中达到37%,因此证明雷达是用于自主车辆中单眼深度估计的有价值的额外传感器。
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Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation among networked devices. Multi-Task Learning (MTL) exploits relevant commonalities across tasks to improve efficiency compared with traditional transfer learning approaches. By learning multiple tasks jointly, significant reduction in terms of energy footprints can be obtained. This article provides a first look into the energy costs of MTL processes driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in distributed wireless networks. The paper targets a clustered multi-task network setup where autonomous agents learn different but related tasks. The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task. This work analyzes the main factors that influence the MTL energy balance by considering a multi-task Reinforcement Learning (RL) setup in a robotized environment. Results show that the MAML method can reduce the energy bill by at least 2 times compared with traditional approaches without inductive transfer. Moreover, it is shown that the optimal energy balance in wireless networks depends on uplink/downlink and sidelink communication efficiencies.
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We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield accurate results only when processing a specific density and distribution of input points, i.e. the one observed during training, narrowing their deployment in real use cases. On the contrary, our solution is robust to uneven distributions and extremely low densities never witnessed during training. Experimental results on standard indoor and outdoor benchmarks highlight the robustness of our framework, achieving accuracy comparable to state-of-the-art methods when tested with density and distribution equal to the training one while being much more accurate in the other cases. Our pretrained models and further material are available in our project page.
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Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.
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Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
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A flexible method is developed to construct a confidence interval for the frequency of a queried object in a very large data set, based on a much smaller sketch of the data. The approach requires no knowledge of the data distribution or of the details of the sketching algorithm; instead, it constructs provably valid frequentist confidence intervals for random queries using a conformal inference approach. After achieving marginal coverage for random queries under the assumption of data exchangeability, the proposed method is extended to provide stronger inferences accounting for possibly heterogeneous frequencies of different random queries, redundant queries, and distribution shifts. While the presented methods are broadly applicable, this paper focuses on use cases involving the count-min sketch algorithm and a non-linear variation thereof, to facilitate comparison to prior work. In particular, the developed methods are compared empirically to frequentist and Bayesian alternatives, through simulations and experiments with data sets of SARS-CoV-2 DNA sequences and classic English literature.
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Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we measured the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage.
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