跨图像建立视觉对应是一项具有挑战性且必不可少的任务。最近，已经提出了大量的自我监督方法，以更好地学习视觉对应的表示。但是，我们发现这些方法通常无法利用语义信息，并且在低级功能的匹配方面过度融合。相反，人类的视觉能够将不同的物体区分为跟踪的借口。受此范式的启发，我们建议学习语义意识的细粒对应关系。首先，我们证明语义对应是通过一组丰富的图像级别自我监督方法隐式获得的。我们进一步设计了一个像素级的自我监督学习目标，该目标专门针对细粒的对应关系。对于下游任务，我们将这两种互补的对应表示形式融合在一起，表明它们是协同增强性能的。我们的方法超过了先前的最先进的自我监督方法，使用卷积网络在各种视觉通信任务上，包括视频对象分割，人姿势跟踪和人类部分跟踪。

加强学习（RL）为决策提供了一个强大的框架，但是其实践中的应用通常需要精心设计的奖励功能。对抗性模仿学习（AIL）阐明了自动策略获取，而无需从环境中访问奖励信号。在这项工作中，我们提出了自动编码的对抗模仿学习（AEAIL），这是一个强大而可扩展的AIL框架。为了从演示中诱导专家政策，AEAIL利用自动编码器的重建误差作为奖励信号，该奖励信号比以前的基于歧视者提供了更多的优化策略信息。随后，我们使用派生的目标函数来训练自动编码器和代理策略。实验表明，与穆约科克环境中的最先进方法相比，我们的AEAIL表现优越。更重要的是，当专家演示嘈杂时，AEAIL表现出更好的鲁棒性。具体而言，我们的方法分别获得了$ 16.4 \％$ $和$ 47.2 \％$相对改进的总体，而最佳基线Fairl和PWIL分别在清洁和嘈杂的专家数据上。视频结果，开源代码和数据集可在https://sites.google.com/view/auto-encoding-imitation中找到。

今天的VIDSGG模型是基于建议的方法，即，它们首先生成众多配对的主题对象片段作为提案，然后对每个提案进行谓词分类。在本文中，我们认为这种普遍的基于建议的框架有三个固有的缺点：1）建议的地面真理谓词标签部分是正确的。 2）他们打破了相同主题对象对的不同谓词实例之间的高阶关系。 3）Vidsgg性能是由提案质量的大约。为此，我们向Vidsgg提出了一个新的分类 - 然后接地框架，可以避免所有三个被忽视的缺点。同时，在此框架下，我们将视频场景图形为临时二分形图形，其中实体和谓词是具有时隙的两种类型的节点，并且边缘在这些节点之间表示不同的语义角色。此配方充分利用了我们的新框架。因此，我们进一步提出了一种基于新的二分曲线图的SGG模型：大。具体而言，大由两部分组成：分类阶段和接地阶段，前者旨在对所有节点和边缘的类别进行分类，并且后者试图本地化每个关系实例的时间位置。两个Vidsgg数据集上的广泛消融已证明我们框架和大的有效性。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

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.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.