各种人类运动预测旨在从一系列观察到的姿势中预测多个可能的未来姿势序列。以前的方法通常采用深层生成网络来对数据的条件分布进行建模，然后从分布中随机取得结果。尽管可以获得不同的结果，但它们通常是最有多样化的结果。最近的工作明确地通过确定性网络了解条件分布的多种模式，但是，该网络只能涵盖有限范围内的固定数量模式。在本文中，我们提出了一种新型的抽样策略，用于对深层生成模型学到的不平衡多模式分布进行采样非常多样化的结果。我们的方法通过生成辅助空间，并巧妙地从目标分布中的多样采样从辅助空间中随机进行随机采样。我们提出了一种简单而有效的网络体系结构，该架构实现了这种新型的采样策略，该策略结合了gumbel-softmax系数矩阵采样方法和促进铰链损失函数的积极多样性。广泛的实验表明，与先前最新的采样方法相比，我们的方法显着提高了采样的多样性和准确性。代码和预训练模型可在https://github.com/droliven/diverse_sampling上找到。

注释大规模数据集以进行监督的视频阴影检测方法是一项挑战。直接使用在标记的图像上训练的模型直接导致高概括错误和时间不一致的结果。在本文中，我们通过提出一个时空插值一致性训练（Stict）框架来解决这些挑战，以合理地将未标记的视频框架以及标记的图像以及图像阴影检测网络训练中进行合理地馈送。具体而言，我们提出了空间和时间ICT，其中定义了两个新的插值方案，\ textit {i.e。}，空间插值和时间插值。然后，我们相应地得出了相应的空间和时间插值一致性约束，以增强像素智能分类任务中的概括和分别鼓励时间一致的预测。此外，我们设计了一个量表感知网络，用于图像中的多尺度阴影知识学习，并提出了比例一致性约束，以最大程度地减少不同尺度上预测之间的差异。我们提出的方法在VISHA数据集和自称数据集上得到了广泛的验证。实验结果表明，即使没有视频标签，我们的方法也比大多数最新的监督，半监督或无监督的图像/视频阴影检测方法以及相关任务中的其他方法更好。代码和数据集可在\ url {https://github.com/yihong-97/stict}上获得。

通过主动学习（AL）获取最具代表性示例，可以通过最大限度地减少图像级或像素 - 明智的注释的努力来使许多数据相关的计算机视觉任务受益。在本文中，我们提出了一种新颖的协作Panoptic-Cable活动学习框架（CPRAL）来解决语义细分任务。对于最初用像素 - WISE注释采样的一小批图像，我们采用Panoptic信息来最初选择未标记的样本。考虑到分段数据集中的类别不平衡，我们导入区域高斯注意模块（RGA）以实现语义偏置选择。该子集通过投票熵突出显示，然后由高斯内核参加，以最大化偏置区域。我们还提出了一个上下文标签扩展（CLE），以提高区域注释，具有语境关注指导。通过协作语义 - 不可知的Panoptic匹配和区域缺陷的选择和延伸，我们的CPRAL可以在标签努力和性能之间取得平衡，并妥协语义分布。我们对城市景观和BDD10K数据集进行了广泛的实验，并表明CPRAL以令人印象深刻的结果和较少的标记比例优于尖端方法。

Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as gains on long-tail object queries, and the ability to perform zero-shot and few-shot NLQ.

Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challenging problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We apply transfer learning to the ConvLSTM network to quickly adapt the trained bidding model to new market environments. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

We propose Hierarchical ProtoPNet: an interpretable network that explains its reasoning process by considering the hierarchical relationship between classes. Different from previous methods that explain their reasoning process by dissecting the input image and finding the prototypical parts responsible for the classification, we propose to explain the reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy. The explanations leverage the hierarchy to deal with uncertainty, akin to human reasoning: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. Experiments on ActivityNet and UCF-101 show performance improvements while providing multi-level explanations.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.