随着几个行业正在朝着建模大规模的3D虚拟世界迈进，因此需要根据3D内容的数量，质量和多样性来扩展的内容创建工具的需求变得显而易见。在我们的工作中，我们旨在训练Parterant 3D生成模型，以合成纹理网格，可以通过3D渲染引擎直接消耗，因此立即在下游应用中使用。 3D生成建模的先前工作要么缺少几何细节，因此在它们可以生成的网格拓扑中受到限制，通常不支持纹理，或者在合成过程中使用神经渲染器，这使得它们在常见的3D软件中使用。在这项工作中，我们介绍了GET3D，这是一种生成模型，该模型直接生成具有复杂拓扑，丰富几何细节和高保真纹理的显式纹理3D网格。我们在可区分的表面建模，可区分渲染以及2D生成对抗网络中桥接了最新成功，以从2D图像集合中训练我们的模型。 GET3D能够生成高质量的3D纹理网格，从汽车，椅子，动物，摩托车和人类角色到建筑物，对以前的方法进行了重大改进。

时间动作本地化在视频分析中起着重要作用，该视频分析旨在将动作定位和分类在未修剪视频中。先前的方法通常可以预测单个时间尺度的特征空间上的动作。但是，低级量表的时间特征缺乏足够的语义来进行动作分类，而高级尺度则无法提供动作边界的丰富细节。为了解决这个问题，我们建议预测多个颞尺度特征空间的动作。具体而言，我们使用不同尺度的精致特征金字塔将语义从高级尺度传递到低级尺度。此外，为了建立整个视频的长时间尺度，我们使用时空变压器编码器来捕获视频帧的远程依赖性。然后，具有远距离依赖性的精制特征被送入分类器以进行粗糙的动作预测。最后，为了进一步提高预测准确性，我们建议使用框架级别的自我注意模块来完善每个动作实例的分类和边界。广泛的实验表明，所提出的方法可以超越Thumos14数据集上的最先进方法，并在ActivityNet1.3数据集上实现可比性的性能。与A2NET（tip20，avg \ {0.3：0.7 \}），sub-action（csvt2022，avg \ {0.1：0.5 \}）和afsd（cvpr21，avg \ {0.3：0.7 \}） ，提出的方法分别可以提高12.6 \％，17.4 \％和2.2 \％

改变布料的人重新识别（REID）是一个新出现的研究主题，旨在检索换衣服的行人。由于带有不同衣服的人类外观表现出较大的变化，因此现有方法很难提取歧视性和健壮的特征表示。当前的作品主要集中在身体形状或轮廓草图上，但是人类的语义信息以及换衣服之前和之后的行人特征的潜在一致性未被充分探索或被忽略。为了解决这些问题，在这项工作中，提出了一种新颖的语义意识到的注意力和视觉屏蔽网络，用于换衣服的人Reid（缩写为SAV），其中关键的想法是屏蔽与衣服外观相关的线索，只关注衣服的外观对视图/姿势变化不敏感的视觉语义信息。具体而言，首先采用了视觉语义编码器来基于人类语义分割信息来定位人体和服装区域。然后，提出了人类的语义注意模块（HSA），以突出显示人类的语义信息并重新授予视觉特征图。此外，视觉服装屏蔽模块（VCS）还旨在通过覆盖衣服区域并将模型集中在与衣服无关的视觉语义信息上来提取更健壮的特征代表。最重要的是，这两个模块在端到端统一框架中共同探索。广泛的实验表明，所提出的方法可以显着胜过最先进的方法，并且可以为换衣的人提取更健壮的特征。与FSAM（在CVPR 2021中发布）相比，该方法可以分别在LTCC和PRCC数据集上以MAP（RANK-1）的形式获得32.7％（16.5％）和14.9％（ - ）。

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.