Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.

Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since the real-world graphs are often a complex mixture of diverse subgraph patterns, learning a universal spectral filter on the graph from the global perspective as in most current works may still suffer from great difficulty in adapting to the variation of local patterns. On the basis of the theoretical analysis on local patterns, we rethink the existing spectral filtering methods and propose the \textbf{\underline{N}}ode-oriented spectral \textbf{\underline{F}}iltering for \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{\underline{N}}etwork (namely NFGNN). By estimating the node-oriented spectral filter for each node, NFGNN is provided with the capability of precise local node positioning via the generalized translated operator, thus discriminating the variations of local homophily patterns adaptively. Meanwhile, the utilization of re-parameterization brings a good trade-off between global consistency and local sensibility for learning the node-oriented spectral filters. Furthermore, we theoretically analyze the localization property of NFGNN, demonstrating that the signal after adaptive filtering is still positioned around the corresponding node. Extensive experimental results demonstrate that the proposed NFGNN achieves more favorable performance.

Unmanned combat air vehicle (UCAV) combat is a challenging scenario with continuous action space. In this paper, we propose a general hierarchical framework to resolve the within-vision-range (WVR) air-to-air combat problem under 6 dimensions of degree (6-DOF) dynamics. The core idea is to divide the whole decision process into two loops and use reinforcement learning (RL) to solve them separately. The outer loop takes into account the current combat situation and decides the expected macro behavior of the aircraft according to a combat strategy. Then the inner loop tracks the macro behavior with a flight controller by calculating the actual input signals for the aircraft. We design the Markov decision process for both the outer loop strategy and inner loop controller, and train them by proximal policy optimization (PPO) algorithm. For the inner loop controller, we design an effective reward function to accurately track various macro behavior. For the outer loop strategy, we further adopt a fictitious self-play mechanism to improve the combat performance by constantly combating against the historical strategies. Experiment results show that the inner loop controller can achieve better tracking performance than fine-tuned PID controller, and the outer loop strategy can perform complex maneuvers to get higher and higher winning rate, with the generation evolves.

Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios by rejecting incorrect predictions. However, we notice a discrepancy between the conventional evaluation vs. the essential purpose of OOD detection. On the one hand, the conventional evaluation exclusively considers risks caused by label-space distribution shifts while ignoring the risks from input-space distribution shifts. On the other hand, the conventional evaluation reward detection methods for not rejecting the misclassified image in the validation dataset. However, the misclassified image can also cause risks and should be rejected. We appeal to rethink OOD detection from a human-centric perspective, that a proper detection method should reject the case that the deep model's prediction mismatches the human expectations and adopt the case that the deep model's prediction meets the human expectations. We propose a human-centric evaluation and conduct extensive experiments on 45 classifiers and 8 test datasets. We find that the simple baseline OOD detection method can achieve comparable and even better performance than the recently proposed methods, which means that the development in OOD detection in the past years may be overestimated. Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.

Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WSI is disease-positive area. However, WSI representation learning still remains an open problem due to: (1) patch sampling on a higher resolution may be incapable of depicting microenvironment information such as the relative position between the tumor cells and surrounding tissues, while patches at lower resolution lose the fine-grained detail; (2) extracting patches from giant WSI results in large bag size, which tremendously increases the computational cost. To solve the problems, this paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Precisely, we randomly extract instant-level patch features from WSIs with different magnification. Then a co-attention mapping between imaging and genomics is learned to uncover the pairwise interaction and reduce the space complexity of imaging features. Such early fusion makes it computationally feasible to use MIL Transformer for the survival prediction task. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability. We evaluate our approach on five cancer types from the Cancer Genome Atlas database and achieved an average c-index of $0.673$, outperforming the state-of-the-art multimodality methods.

对抗性训练（AT）通常被认为是防御对抗性例子的最有效的方法之一，可能会在很大程度上损害标准绩效，因此对工业规模的生产和应用的有用性有限。令人惊讶的是，这种现象在自然语言处理（NLP）任务中完全相反，在该任务中甚至可以从中受益。我们注意到NLP任务中AT的优点可能来自离散和符号输入空间。为了借用NLP风格的优势，我们提出了离散的对抗训练（DAT）。 DAT利用VQGAN改革图像数据以离散类似文本的输入，即视觉单词。然后，它可以最大程度地减少这种离散图像的最大风险，并具有符号对抗扰动。我们从分布的角度进一步提供了解释，以证明DAT的有效性。作为增强视觉表示的插件技术，DAT可以在多个任务上取得重大改进，包括图像分类，对象检测和自我监督学习。尤其是，该模型通过胶带自动编码（MAE）预先训练并由我们的DAT进行微调，而没有额外的数据可以在Imagenet-C上获得31.40 MCE，并且在Stylized-Imagenet上进行了32.77％的TOP-1准确性，建立了新的状态 - 艺术。该代码将在https://github.com/alibaba/easyrobust上找到。

传统的LIDAR射测（LO）系统主要利用从经过的环境获得的几何信息来注册激光扫描并估算Lidar Ego-Motion，而在动态或非结构化环境中可能不可靠。本文提出了Inten-loam，一种低饮用和健壮的激光镜和映射方法，该方法完全利用激光扫描的隐式信息（即几何，强度和时间特征）。扫描点被投影到圆柱形图像上，这些图像有助于促进各种特征的有效和适应性提取，即地面，梁，立面和反射器。我们提出了一种新型基于强度的点登记算法，并将其纳入LIDAR的探光仪，从而使LO系统能够使用几何和强度特征点共同估计LIDAR EGO-MOTION。为了消除动态对象的干扰，我们提出了一种基于时间的动态对象删除方法，以在MAP更新之前过滤它们。此外，使用与时间相关的体素网格滤波器组织并缩减了本地地图，以维持当前扫描和静态局部图之间的相似性。在模拟和实际数据集上进行了广泛的实验。结果表明，所提出的方法在正常驾驶方案中实现了类似或更高的精度W.R.T，在非结构化环境中，最先进的方法优于基于几何的LO。

这项工作解决了中央机器学习问题的问题，即在分布（OOD）测试集上的性能降解问题。这个问题在基于医学成像的诊断系统中尤为明显，该系统似乎是准确的，但在新医院/数据集中进行测试时失败。最近的研究表明，该系统可能会学习快捷方式和非相关功能，而不是可推广的功能，即所谓的良好功能。我们假设对抗性训练可以消除快捷方式功能，而显着性训练可以滤除非相关功能。两者都是OOD测试集的性能降解的滋扰功能。因此，我们为深度神经网络制定了一种新颖的模型培训方案，以学习分类和/或检测任务的良好功能，以确保在OOD测试集上的概括性性能。实验结果定性和定量证明了我们使用基准CXR图像数据集在分类任务上的基准CXR图像数据集的出色性能。

近年来，多视图学习迅速发展。尽管许多先前的研究都认为每个实例都出现在所有视图中，但在现实世界应用程序中很常见，从某些视图中丢失实例，从而导致多视图数据不完整。为了解决这个问题，我们提出了一个新型潜在的异质图网络（LHGN），以实现不完整的多视图学习，该学习旨在以灵活的方式尽可能充分地使用多个不完整的视图。通过学习统一的潜在代表，隐含地实现了不同观点之间一致性和互补性之间的权衡。为了探索样本与潜在表示之间的复杂关系，首次提出了邻域约束和视图约束，以构建异质图。最后，为了避免训练和测试阶段之间的任何不一致之处，基于图形学习的分类任务应用了转导学习技术。对现实世界数据集的广泛实验结果证明了我们模型对现有最新方法的有效性。