In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image. In particular, we firstly evaluate different low complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2, NASNetMobile, and EfficientNetB0, which present the number of trainable parameters lower than 5 Million (M). After indicating best network architecture, we further improve the network performance by applying attention schemes to multiple feature maps extracted from middle layers of the network. To deal with the issue of increasing the model footprint as using attention schemes, we apply the quantization technique to satisfies the number trainable parameter of the model lower than 5 M. By conducting extensive experiments on the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity model, which is very competitive to the state-of-the-art systems and potential for real-life applications on edge devices.

我们分析了一个随机近似算法的决策依赖性问题，其中算法沿迭代序列演变的数据分布。此类问题的主要示例出现在表演预测及其多人游戏扩展中。我们表明，在温和的假设下，算法的平均迭代和溶液之间的偏差在渐近正常上，协方差很好地解除了梯度噪声和分布移位的影响。此外，在H \'Ajek和Le Cam的工作中，我们表明该算法的渐近性能是本地最小的最佳选择。

互动学习和决策的基本挑战，从强盗问题到加固学习，是提供了实现的采样效率，自适应学习算法，实现了近乎最佳的遗憾。这个问题类似于最佳（监督）统计学习的经典问题，其中有众所周知的复杂性措施（例如，VC维度和Rademacher复杂性），用于控制学习的统计复杂性。然而，由于问题的适应性，表征交互式学习的统计复杂性基本上更具挑战性。这项工作的主要结果提供了复杂性措施，决策系数，被证明是必要的，并且足以用于采样有效的互动学习。特别是，我们提供：1。对于任何交互式决策问题的最佳遗憾的下限，将决策估计系数作为基本限制建立。 2.统一算法设计原理，估算到决策（E2D），它将任何用于监督估算的算法转换为决策的在线算法。 E2D遗憾的是符合我们下限的遗憾，从而实现了最佳的样本高效学习，其特征在于决策估计系数。一起参加，这些结果构成了互动决策的可读性理论。当应用于增强学习设置时，决策估计系数基本上恢复所有现有的硬度结果和下限。更广泛地，该方法可以被视为统计估算的经典LE CAM理论的决策理论;它还统一了许多现有方法 - 贝叶斯和频繁的方法。

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.

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task in computer vision. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without any further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel framework called CLIP-ES for WSSS. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) to mitigate noise and focus on confident regions. Our proposed framework dramatically reduces the cost of training for WSSS and shows the capability of localizing objects in CLIP. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.

This paper describes Waymo's Collision Avoidance Testing (CAT) methodology: a scenario-based testing method that evaluates the safety of the Waymo Driver Automated Driving Systems' (ADS) intended functionality in conflict situations initiated by other road users that require urgent evasive maneuvers. Because SAE Level 4 ADS are responsible for the dynamic driving task (DDT), when engaged, without immediate human intervention, evaluating a Level 4 ADS using scenario-based testing is difficult due to the potentially infinite number of operational scenarios in which hazardous situations may unfold. To that end, in this paper we first describe the safety test objectives for the CAT methodology, including the collision and serious injury metrics and the reference behavior model representing a non-impaired eyes on conflict human driver used to form an acceptance criterion. Afterward, we introduce the process for identifying potentially hazardous situations from a combination of human data, ADS testing data, and expert knowledge about the product design and associated Operational Design Domain (ODD). The test allocation and execution strategy is presented next, which exclusively utilize simulations constructed from sensor data collected on a test track, real-world driving, or from simulated sensor data. The paper concludes with the presentation of results from applying CAT to the fully autonomous ride-hailing service that Waymo operates in San Francisco, California and Phoenix, Arizona. The iterative nature of scenario identification, combined with over ten years of experience of on-road testing, results in a scenario database that converges to a representative set of responder role scenarios for a given ODD. Using Waymo's virtual test platform, which is calibrated to data collected as part of many years of ADS development, the CAT methodology provides a robust and scalable safety evaluation.

卷积神经网络（CNN）成为计算机视觉最受欢迎和最突出的深度学习体系结构之一，但其黑匣子功能隐藏了内部预测过程。因此，AI从业者阐明了可解释的AI，以提供模型行为的解释性。特别是，基于类的激活图（CAM）和基于GRAD-CAM的方法已显示出希望结果，但它们具有架构限制或梯度计算负担。为了解决这些问题，已建议将得分摄像机作为一种无梯度方法，但是，与基于CAM或GRAD-CAM的方法相比，它需要更多的执行时间。因此，我们通过空间掩盖提取的特征图来利用激活图和网络输出之间的相关性，提出了一个轻巧的体系结构和无梯度的互惠凸轮（配克CAM）。通过提出的方法，与平均跌落 - 相干 - 复杂性（ADCC）度量相比，Resnet家族中的1：78-3：72％的收益不包括VGG-16（1：39％）（1：39％） ）。此外，配置摄像头表现出与Grad-CAM相似的显着性图生成速率，并且比Score-CAM快于148倍。

大多数现有的语义分割方法都以图像级类标签作为监督，高度依赖于从标准分类网络生成的初始类激活图（CAM）。在本文中，提出了一种新颖的“渐进贴片学习”方法，以改善分类的局部细节提取，从而更好地覆盖整个对象的凸轮，而不仅仅是在常规分类模型中获得的CAM中的最歧视区域。 “补丁学习”将特征映射破坏成贴片，并在最终聚合之前并行独立处理每个本地贴片。这样的机制强迫网络从分散的歧视性本地部分中找到弱信息，从而提高了本地细节的敏感性。 “渐进的补丁学习”进一步将特征破坏和补丁学习扩展到多层粒度。与多阶段优化策略合作，这种“渐进的补丁学习”机制隐式地为模型提供了跨不同位置粒状性的特征提取能力。作为隐式多粒性渐进式融合方法的替代方案，我们还提出了一种明确的方法，以同时将单个模型中不同粒度的特征融合，从而进一步增强了完整对象覆盖的凸轮质量。我们提出的方法在Pascal VOC 2012数据集上取得了出色的性能，例如，测试集中有69.6 $％miou），它超过了大多数现有的弱监督语义细分方法。代码将在此处公开提供，https://github.com/tyroneli/ppl_wsss。

生成精确的类感知的伪基真实，也就是类激活图（CAM），对于弱监督的语义分割至关重要。原始CAM方法通常会产生不完整和不准确的定位图。为了解决这个问题，本文提出了基于可变形卷积中的偏移学习的扩展和收缩方案，以依次改善两个各个阶段中定位对象的回忆和精度。在扩展阶段，在可变形卷积层中的偏移学习分支，称为“扩展采样器”，寻求采样越来越小的判别对象区域，这是由逆监督信号驱动的，从而最大程度地提高了图像级分类损失。然后在收缩阶段逐渐将位置更完整的物体逐渐缩小到最终对象区域。在收缩阶段，引入了另一个可变形卷积层的偏移学习分支，称为“收缩采样器”，以排除在扩展阶段参加的假积极背景区域，以提高定位图的精度。我们在Pascal VOC 2012和MS Coco 2014上进行了各种实验，以很好地证明了我们方法比其他最先进的方法对弱监督语义分割的优越性。代码将在此处公开提供，https://github.com/tyroneli/esol_wsss。