可控的人图像合成任务可以通过对身体姿势和外观的明确控制来实现广泛的应用。在本文中，我们提出了一个基于跨注意的样式分布模块，该模块在源语义样式和目标姿势转移的目标姿势之间计算。该模块故意选择每个语义表示的样式，并根据目标姿势分配它们。交叉注意的注意力矩阵表达了目标姿势与所有语义的源样式之间的动态相似性。因此，可以利用它来从源图像路由颜色和纹理，并受到目标解析图的进一步限制，以实现更清晰的目标。同时，为了准确编码源外观，还添加了不同语义样式之间的自我注意力。我们的模型的有效性在姿势转移和虚拟的尝试任务上进行了定量和质量验证。

在本文中，我们提出了与IEEE计算机协会在CVPR 2022上同时与IEEE计算机协会研讨会同时举行的多手术检测挑战。我们的多手术检测挑战旨在检测自动图像操作，包括但不限于图像编辑，图像合成，图像合成，图像，图像，图像，图像合成，图像，图像编辑一代，图像Photoshop等。我们的挑战吸引了来自世界各地的674支团队，约有2000个有效的结果提交数量。我们邀请了前十支球队为挑战提供解决方案，其中三支球队在大结局中获得了奖项。在本文中，我们介绍了前三名团队的解决方案，以增强图像伪造检测领域的研究工作。

深度神经网络（DNN）已被证明是针对对抗性示例（AE）的脆弱性，这些例子是恶意设计用于欺骗目标模型的。添加了不可察觉的对抗扰动的正常示例（NES）可能是对DNN的安全威胁。尽管现有的AES检测方法已经达到了很高的精度，但他们未能利用检测到的AE的信息。因此，基于高维扰动提取，我们提出了一种无模型的AES检测方法，其整个过程没有查询受害者模型。研究表明，DNN对高维度敏感。对抗示例中隐藏的对抗性扰动属于高维特征，高维特征是高度预测性和非持胸膜的。 DNN比其他人从高维数据中学习更多细节。在我们的方法中，扰动提取器可以从AES作为高维特征提取对抗扰动，然后训练有素的AES鉴别器确定输入是否为AE。实验结果表明，所提出的方法不仅可以以高精度检测对抗示例，还可以检测AE的特定类别。同时，提取的扰动可用于将AE恢复到NES。

交通标志检测是无人驾驶系统的具有挑战性的任务，特别是对于检测多尺度目标和检测的实时问题。在交通标志检测过程中，目标的比例大大变化，这将对检测精度产生一定的影响。特征金字塔广泛用于解决这个问题，但它可能会破坏不同的交通标志尺度的功能一致性。此外，在实际应用中，常用方法难以提高多尺度交通标志的检测精度，同时确保实时检测。在本文中，我们提出了一种改进的特征金字塔模型，名为AF-FPN，它利用自适应注意模块（AAM）和特征增强模块（FEM）来减少特征映射生成过程中的信息损失，并提高表示能力特征金字塔。我们用AF-FPN替换了YOLOV5中的原始特征金字塔网络，这在确保实时检测的前提下提高了YOLOV5网络的多尺度目标的检测性能。此外，提出了一种新的自动学习数据增强方法来丰富数据集，提高模型的稳健性，使其更适合实际情况。关于清华腾讯100K（TT100K）数据集的广泛实验结果证明了与多种最先进的方法相比，所提出的方法的有效性和优越性。

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.