In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.
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Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.
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The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.
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With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.
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The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The standard way is to unroll an iterative algorithm into a neural network with a forward model embedded. The forward model always changes in clinical practice, so the learning component's entanglement with the forward model makes the reconstruction hard to generalize. The proposed method is more generalizable for different MR acquisition settings by separating the forward model from the deep learning component. The deep learning-based proximal gradient descent was proposed to create a learned regularization term independent of the forward model. We applied the one-time trained regularization term to different MR acquisition settings to validate the proposed method and compared the reconstruction with the commonly used $\ell_1$ regularization. We showed ~3 dB improvement in the peak signal to noise ratio, compared with conventional $\ell_1$ regularized reconstruction. We demonstrated the flexibility of the proposed method in choosing different undersampling patterns. We also evaluated the effect of parameter tuning for the deep learning regularization.
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对具有代理商初始位置未知的有限3D环境的多代理探索是一个具有挑战性的问题。它需要快速探索环境，并坚定合并代理商构建的子图。我们认为现有方法是侵略性或保守的：在检测到重叠时，积极的策略合并了两种由不同代理构建的子图，这可能导致由于对重叠的错误阳性检测而导致不正确的合并，因此是如此。不健全。保守策略指导一个代理人在合并之前重新审视另一个代理商的过量验证历史轨迹，这可以降低由于对同一空间的反复探索而引起的勘探效率。为了巧妙地平衡子图合并和勘探效率的鲁棒性，我们为基于激光雷达的多代理探索开发了一种新方法，该方法可以指导一个代理商以\ emph {自适应}方式重复另一个代理商的轨迹子图合并过程的指标。此外，我们的方法通过计划合并子图的代理人共同计划，以进一步提高勘探效率，以\ emph {Cooperative}方式将最近的单格分层勘探策略扩展到多个代理。我们的实验表明，我们的方法平均比基线高出50 \％，同时稳固地合并子映射。
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热应力和变形的快速分析在热控制措施和卫星结构设计的优化中起着关键作用。为了实现卫星主板的实时热应力和热变形分析，本文提出了一种新型的多任务注意UNET（MTA-UNET）神经网络，将多任务学习（MTL）和U-NET的优势结合在一起注意机制。此外，在训练过程中使用了物理知识的策略，其中部分微分方程（PDE）被整合到损失函数中作为残留项。最后，将基于不确定性的损失平衡方法应用于重量的多个培训任务的不同损失功能。实验结果表明，与单任务学习（STL）模型相比，提出的MTA-UNET有效提高了多个物理任务的预测准确性。此外，物理信息的方法在每个任务的预测中的错误较小，尤其是在小型数据集上。代码可以在：\ url {https://github.com/komorebitso/mta-unet}下载。
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神经辐射场（NERF）及其变体在代表3D场景和合成照片现实的小说视角方面取得了巨大成功。但是，它们通常基于针孔摄像头模型，并假设全焦点输入。这限制了它们的适用性，因为从现实世界中捕获的图像通常具有有限的场地（DOF）。为了减轻此问题，我们介绍了DOF-NERF，这是一种新型的神经渲染方法，可以处理浅的DOF输入并可以模拟DOF效应。特别是，它扩展了NERF，以模拟按照几何光学的原理模拟镜头的光圈。这样的物理保证允许DOF-NERF使用不同的焦点配置操作视图。 DOF-NERF受益于显式光圈建模，还可以通过调整虚拟光圈和焦点参数来直接操纵DOF效果。它是插件，可以插入基于NERF的框架中。关于合成和现实世界数据集的实验表明，DOF-NERF不仅在全焦点设置中与NERF相当，而且可以合成以浅DOF输入为条件的全焦点新型视图。还展示了DOF-nerf在DOF渲染上的有趣应用。源代码将在https://github.com/zijinwuzijin/dof-nerf上提供。
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基于变压器的视觉对象跟踪已广泛使用。但是，变压器结构缺乏足够的电感偏差。此外，仅专注于编码全局功能会损害建模本地细节，这限制了航空机器人中跟踪的能力。具体而言，通过局部模型为全球搜索机制，提出的跟踪器将全局编码器替换为新型的局部识别编码器。在使用的编码器中，仔细设计了局部识别的关注和局部元素校正网络，以减少全局冗余信息干扰和增加局部归纳偏见。同时，后者可以通过详细信息网络准确地在空中视图下对本地对象详细信息进行建模。所提出的方法在几种权威的空中基准中实现了竞争精度和鲁棒性，总共有316个序列。拟议的跟踪器的实用性和效率已通过现实世界测试得到了验证。
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被遮挡的人重新识别（RE-ID）旨在解决跨多个摄像机感兴趣的人时解决遮挡问题。随着深度学习技术的促进和对智能视频监视的需求的不断增长，现实世界应用中的频繁闭塞使闭塞的人重新引起了研究人员的极大兴趣。已经提出了大量封闭的人重新ID方法，而很少有针对遮挡的调查。为了填补这一空白并有助于提高未来的研究，本文提供了对封闭者重新ID的系统调查。通过对人体闭塞的深入分析，发现大多数现有方法仅考虑一部分闭塞问题。因此，我们从问题和解决方案的角度回顾了与闭塞相关的人重新ID方法。我们总结了个人重新闭塞引起的四个问题，即位置错位，规模错位，嘈杂的信息和缺失的信息。然后对解决不同问题的闭塞相关方法进行分类和引入。之后，我们总结并比较了四个流行数据集上最近被遮挡的人重新ID方法的性能：部分reid，部分易边，咬合 - 固定和遮挡的dukemtmc。最后，我们提供了有关有希望的未来研究方向的见解。
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