接近周期性的模式（NPP）在人造场景中无处不在，由瓷砖图案组成，其外观差异是由照明，缺陷或设计元素引起的。良好的NPP表示对许多应用程序有用，包括图像完成，分割和几何重新映射。但是代表NPP是具有挑战性的，因为它需要保持全球一致性（瓷砖图案布局），同时保留局部变化（外观差异）。使用大型数据集或单图像优化斗争在一般场景上训练的方法以满足这些约束，而明确模型周期性的方法对周期性检测错误并不强大。为了应对这些挑战，我们使用基于坐标的MLP学习具有单图像优化的神经隐式表示。我们设计一个输入功能翘曲模块和周期性指导的补丁损失，以处理全球一致性和局部变化。为了进一步提高鲁棒性，我们引入了一个周期性建议模块，以在我们的管道中搜索和使用多个候选周期。我们在单个和多平面场景上展示了我们方法对500多个建筑物，架子，壁纸，地面和蒙德里安图案的有效性。

可区分的渲染器在对象的3D表示和该对象的图像之间提供了直接的数学链接。在这项工作中，我们为紧凑的，可解释的表示形式开发了一个近似可区分的渲染器，我们称之为模糊的metaballs。我们的大约渲染器着重于通过深度图和轮廓渲染形状。它牺牲了为实用程序提供忠诚，生成快速运行时间和可用于解决视觉任务的高质量梯度信息。与基于网格的可区分渲染器相比，我们的方法具有更快的5倍，向后传球的速度快30倍。我们方法生成的深度图和轮廓图像在任何地方都平滑且定义。在我们对可区分渲染器进行姿势估计的评估时，我们表明我们的方法是唯一与经典技术相媲美的方法。在Silhouette的形状上，我们的方法仅使用梯度下降和每像素损失，而没有任何替代损失或正则化。这些重建即使在具有分割工件的自然视频序列上也很好地工作。项目页面：https：//leonidk.github.io/fuzzy-metaballs

几何和语义上的全面3D场景理解对于机器人感知等现实世界应用都很重要。现有的大多数工作都集中在开发以数据驱动的判别模型来理解现场。从合成模型的角度来看，本文通过利用隐式3D表示和神经渲染的最新进展，提供了一种新的场景理解方法。在神经辐射场（NERFS）的巨大成功之下，我们与NERF（SS-NERF）介绍了场景 - 陶艺合成，不仅能够从新颖的角度呈现照片真实的RGB图像，还可以使各种准确的场景属性（例如，外观，几何和语义）。通过这样做，我们便有助于解决统一框架下的各种场景理解任务，包括语义分割，表面正常估计，重新载体，键盘检测和边缘检测。我们的SS-NERF框架可以成为弥合生成学习和歧视性学习的强大工具，因此有益于研究广泛有趣的问题，例如在综合范式中研究任务关系，将知识转移到新颖的任务中，促进知识，促进下游判别任务是数据增强的方式，并作为数据创建的自动标签者。

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset. Code, data and trained models are available at https://wentaoyuan.github.io/pcn.

Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning ("learning to learn") by combining a meta-learner with a "hallucinator" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark.

In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

背景：Covid-19已成为全球挑战，并妥善规划医疗资源是打击Covid-19的关键。在美国退伍军人事务保健系统（VA）中，许多登记者易受Covid-19的影响。预测Covid-19迅速分配医疗资源成为一个关键问题。当VA登记者有Covid-19症状时，建议他们的第一步应该是调用VA呼叫中心。对于确认的Covid-19患者，从第一个症状到医院入院的中位时间为七天。通过预测Covid-19相关电话的数量，我们可以预测医疗保健使用和计划前方的迫在眉睫。目的：该研究旨在开发一种方法来预测110名VA医疗中心中的每一个的Covid-19相关电话的每日数量。方法：在该方法中，我们使用一组医疗中心预先训练模型，并为个别医疗中心进行微调。在群集级别，我们执行了功能选择，以选择更大的功能和自动超参数搜索，以选择模型的最佳超参数值组合。结论：本研究提出了一种准确的方法，预测VA医疗中心的每日Covid-19相关呼叫数量。该方法能够通过将类似的医疗中心分组成群组来克服建模挑战，以扩大培训模型的数据集，并使用超参数搜索自动查找模型的最佳超参数值组合。通过提出的方法，可以预先预测医疗保健的潮。这使得保健从业者能够更好地计划医疗资源和战斗Covid-19。