Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning, related methods have good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on deep learning difficult. Therefore, we propose a Benchmark for Airport Runway Segmentation, named BARS. Meanwhile, a semi-automatic annotation pipeline is designed to reduce the workload of annotation. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which is collected using the X-Plane simulation platform, contains 10,002 images and 29,347 instances with three categories. We evaluate eight representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of the airport runway with a regular shape, we propose a plug-and-play smoothing post-processing module (SPPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPPM and CPCL can improve the average accuracy by 0.9% and 1.13%, respectively. And the average smoothness enhancements for SPPM and CPCL are more than 50% and 28%, respectively.

卷积神经网络（CNN）已经实现了医学图像细分的最先进性能，但需要大量的手动注释进行培训。半监督学习（SSL）方法有望减少注释的要求，但是当数据集大小和注释图像的数量较小时，它们的性能仍然受到限制。利用具有类似解剖结构的现有注释数据集来协助培训，这有可能改善模型的性能。然而，由于目标结构的外观不同甚至成像方式，跨解剖结构域的转移进一步挑战。为了解决这个问题，我们提出了跨解剖结构域适应（CS-CADA）的对比度半监督学习，该学习适应一个模型以在目标结构域中细分相似的结构，这仅需要通过利用一组现有现有的现有的目标域中的限制注释源域中相似结构的注释图像。我们使用特定领域的批归归量表（DSBN）来单独地标准化两个解剖域的特征图，并提出跨域对比度学习策略，以鼓励提取域不变特征。它们被整合到一个自我兼容的均值老师（SE-MT）框架中，以利用具有预测一致性约束的未标记的目标域图像。广泛的实验表明，我们的CS-CADA能够解决具有挑战性的跨解剖结构域移位问题，从而在视网膜血管图像和心脏MR图像的帮助下，在X射线图像中准确分割冠状动脉，并借助底底图像，分别仅给定目标域中的少量注释。

Deep Q-Network（DQN）算法解决的大规模实践工作表明，随机政策尽管简单，但最常用的探索方法是最常用的探索方法。但是，大多数现有的随机探索方法要么探索新的动作，无论Q值如何，要么不可避免地将偏见引入学习过程中，以将抽样与Q值搭配。在本文中，我们提出了一种新颖的偏好指导$ \ epsilon $ greedy Exploration算法，该算法可以在不引入其他偏见的情况下根据Q值的Q值有效地学习动作分布。具体而言，我们设计了一个由两个分支组成的双重体系结构，其中一个是DQN的副本，即Q Branch。我们称为首选项分支的另一个分支，了解了DQN隐式所遵循的动作偏好。从理论上讲，我们证明了策略改进定理适用于首选项指导的$ \ epsilon $ greedy策略，并在实验上表明，推断的动作偏好分布与相应的Q值的景观保持一致。因此，偏好引导的$ \ epsilon $ - 秘密探索激励DQN代理采取多种操作，即可以更频繁地采样较大的Q值的行动，而使用较小的Q值的行动仍然可以探索，因此仍有机会。鼓励探索。我们在九个不同的环境中使用四个众所周知的DQN变体评估了提出的方法。广泛的结果证实了我们提出的方法在性能和收敛速度方面的优势。索引术语 - 偏好引导的探索，随机政策，数据效率，深度强化学习，深度Q学习。

检测定向对象以及估计其旋转信息是用于分析遥感图像的一个关键步骤。尽管最近提出了许多方法，但大多数人直接学习在仅单独的一个（例如旋转角度）的监督下预测对象方向或仅为几（例如旋转角度）或几（例如若干坐标）地基值。在训练期间采用了关于提议和旋转信息回归的额外约束，在额外约束，在训练期间采用了更准确的对象检测。为此，我们创新地提出了一种通过Naive几何计算以一致的方式同时学习物体的水平提出，面向建议和旋转角度的机制，作为一个额外的稳定约束（参见图1）。提出了一个导向的中心先前引导标签分配策略，以进一步提高建议的质量，产生更好的性能。广泛的实验表明，配备我们的想法的模型显着优于基线，通过大幅度来实现新的最先进的结果，在推理期间没有任何额外的计算负担。我们提出的想法简单直观，可以随时实现。源代码和培训的型号涉及补充文件。

深度神经网络通常需要准确和大量注释，以在医学图像分割中实现出色的性能。单次分割和弱监督学习是有前途的研究方向，即通过仅从一个注释图像学习新类并利用粗标签来降低标签努力。以前的作品通常未能利用解剖结构并遭受阶级不平衡和低对比度问题。因此，我们为3D医学图像分割的创新框架提供了一次性和弱监督的设置。首先，提出了一种传播重建网络，以基于不同人体中的解剖模式类似的假设将来自注释体积的划痕投射到未标记的3D图像。然后，双级功能去噪模块旨在基于解剖结构和像素级别来改进涂鸦。在将涂鸦扩展到伪掩码后，我们可以使用嘈杂的标签培训策略培训新课程的分段模型。一个腹部的实验和一个头部和颈部CT数据集显示所提出的方法对最先进的方法获得显着改善，即使在严重的阶级不平衡和低对比度下也能够稳健地执行。

尽管深度神经网络（DNN）在各种应用中取得了突出的性能，但众所周知，DNN易于在清洁/原始样品中具有难以察觉的扰动的对抗性实施例/样品（AES）。克服对抗对抗攻击的现有防御方法的弱点，这破坏了原始样本的信息，导致目标分类器精度的减少，提高了增强的反对对抗攻击方法IDFR（通过输入去噪和功能恢复） 。所提出的IDFR是由增强型输入丹麦优化的增强型输入丹麦（ID）和隐藏的有损特征恢复器（FR）组成。在基准数据集上进行的广泛实验表明，所提出的IDFR优于各种最先进的防御方法，对保护目标模型免受各种对抗黑盒或白盒攻击的高度有效。 \脚注{souce代码释放：\ href {https://github.com/id-fr/idfr} {https：//github.com/id-fr/idfr}}

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.

This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features with a U-Net model as the primary node feature extraction module, followed by a successive spectral-based graph convolutional network. To create a diverse set of filters, we use anisotropic wavelet basis filters, being sensitive to both different directions and band-passes. This filter set overcomes the over-smoothing behavior of conventional graph neural networks. To further improve the model's performance, we add a function that perturbs the feature maps in the last layer ahead of fully connected layers, forcing the network to learn more discriminative features overall. The resulting correspondence maps show state-of-the-art performance on the benchmark datasets based on average geodesic errors and superior robustness to discretization in 3D meshes. Our approach provides new insights and practical solutions to the dense shape correspondence research.

早期退出是提高深网推理效率的有效范例。通过构建具有不同资源需求的分类器（出口），此类网络可以在早期出口处输出简单的样本，从而消除了执行更深层的需求。尽管现有作品主要关注多EXIT网络的建筑设计，但此类模型的培训策略在很大程度上没有探索。当前的最新模型在培训期间对所有样品进行了相同的处理。但是，在测试过程中的早期外观行为被忽略了，从而导致训练和测试之间存在差距。在本文中，我们建议通过样品加权来弥合这一差距。从直觉上讲，简单的样品通常在推理期间在网络早期退出，应该为培训早期分类器提供更多贡献。但是，晚期分类器应强调硬样品的培训（主要是从更深层退出）。我们的工作建议采用一个体重预测网络，以加重每个出口处不同训练样本的损失。这个重量预测网络和骨干模型在具有新的优化目标的元学习框架下共同优化。通过将推断期间的适应性行为带入训练阶段，我们表明拟议的加权机制始终提高分类准确性和推理效率之间的权衡。代码可在https://github.com/leaplabthu/l2w-den上找到。