具有自动化和连通性的赋予，连接和自动化的车辆旨在成为合作驾驶自动化的革命性推动者。然而，骑士需要对周围环境的高保真感知信息，但从各种车载传感器以及车辆到所有的通信（v2x）通信中都可以昂贵。因此，通过具有成本效益的平台基于高保真传感器的真实感知信息对于启用与CDA相关的研究（例如合作决策或控制）至关重要。大多数针对CAVS的最先进的交通模拟研究都通过直接呼吁对象的内在属性来依赖情况 - 意识信息，这阻碍了CDA算法评估的可靠性和保真度。在这项研究中，\ textit {网络移动镜（CMM）}共模拟平台设计用于通过提供真实感知信息来启用CDA。 \ textit {cmm}共模拟平台可以通过高保真传感器感知系统和具有实时重建系统的网络世界模仿现实世界。具体而言，现实世界的模拟器主要负责模拟交通环境，传感器以及真实的感知过程。 Mirror-World Simulator负责重建对象，并将其信息作为模拟器的内在属性，以支持CD​​A算法的开发和评估。为了说明拟议的共模拟平台的功能，将基于路边的激光雷达的车辆感知系统原型作为研究案例。特定的流量环境和CDA任务是为实验设计的，其结果得到了证明和分析以显示平台的性能。

在本文中，我们首先尝试调查深度哈希学习与车辆重新识别的集成。我们提出了一个深度哈希的车辆重新识别框架，被称为DVHN，这基本上减少了存储器使用，并在预留最接近的邻居搜索精度的同时提高检索效率。具体地，〜DVHN通过联合优化特征学习网络和哈希码生成模块，直接为每个图像直接学习离散的紧凑型二进制哈希代码。具体地，我们直接将来自卷积神经网络的输出限制为离散二进制代码，并确保学习的二进制代码是对分类的最佳选择。为了优化深度离散散列框架，我们进一步提出了一种用于学习二进制相似性保存散列代码的交替最小化方法。在两个广泛研究的车辆重新识别数据集 - \ textbf {sportid}和\ textbf {veri} - 〜〜\ textbf {veri} - 〜已经证明了我们对最先进的深哈希方法的方法的优越性。 2048美元的TextBF {DVHN}价格可以实现13.94 \％和10.21 \％的准确性改进\ textbf {map}和\ textbf {stuckbf {stank @ 1}的\ textbf {stuckid（800）} dataSet。对于\ textbf {veri}，我们分别实现了35.45 \％和32.72 \％\ textbf {rank @ 1}和\​​ textbf {map}的性能增益。

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: 1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; 2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and 3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. A cooperative perception platform is designed and developed for CP dataset acquisition and several baselines are compared during the experiments. The experimental analysis shows that VINet can achieve remarkable improvements for pedestrians and cars with 2x less system-wide computational costs and 12x less system-wide communicational costs.

红外小目标检测（ISTD）是重要的计算机视觉任务。 ISTD旨在将小目标与复杂的背景混乱区分开。红外辐射在距离上衰减，使目标高度变暗，容易与背景混乱混淆，这使得探测器具有挑战性，以平衡精度和召回率。为了解决这一困难，本文提出了一种基于神经网络的ISTD方法，称为CourtNet，该方法具有三个子网络：起诉网络旨在提高召回率；被告网络致力于提高精度率。陪审团网络加权他们的结果，以适应精确度和召回率。此外，起诉网络还利用了密集的连接变压器结构，这可以防止小目标在网络正向传播中消失。另外，采用细粒度的注意模块来准确定位小目标。实验结果表明，CourtNet在两个ISTD数据集（0.62）和SIRST（0.73）上达到了最佳的F1得分。

强有力的对手例子是评估和增强深神经网络鲁棒性的关键。流行的对抗性攻击算法使用梯度上升最大化非cave损失函数。但是，每种攻击的性能通常对由于信息不足（仅一个输入示例，几乎没有白色盒子源模型和未知的防御策略）而敏感。因此，精心设计的对抗性示例容易过度拟合源模型，从而将其转移性限制在身份不明的架构上。在本文中，我们提出了多种渐近正态分布攻击（Multianda），这是一种新颖的方法，可以明确表征来自学习分布的对抗性扰动。具体而言，我们通过利用随机梯度上升（SGA）的渐近正态性能（SGA）的优势来近似于扰动，然后将整体策略应用于此过程，以估算高斯混合模型，以更好地探索潜在的优化空间。从学习分布中绘制扰动使我们能够为每个输入生成任何数量的对抗示例。近似后验实质上描述了SGA迭代的固定分布，该分布捕获了局部最佳距离周围的几何信息。因此，从分布中得出的样品可靠地保持转移性。我们提出的方法通过对七个正常训练和七个防御模型进行广泛的实验，超过了对具有或没有防御的深度学习模型的九个最先进的黑盒攻击。

感知环境是实现合作驾驶自动化（CDA）的最基本关键之一，该关键被认为是解决当代运输系统的安全性，流动性和可持续性问题的革命性解决方案。尽管目前在计算机视觉的物体感知领域正在发生前所未有的进化，但由于不可避免的物理遮挡和单辆车的接受程度有限，最先进的感知方法仍在与复杂的现实世界流量环境中挣扎系统。基于多个空间分离的感知节点，合作感知（CP）诞生是为了解锁驱动自动化的感知瓶颈。在本文中，我们全面审查和分析了CP的研究进度，据我们所知，这是第一次提出统一的CP框架。审查了基于不同类型的传感器的CP系统的体系结构和分类学，以显示对CP系统的工作流程和不同结构的高级描述。对节点结构，传感器模式和融合方案进行了审查和分析，并使用全面的文献进行了详细的解释。提出了分层CP框架，然后对现有数据集和模拟器进行审查，以勾勒出CP的整体景观。讨论重点介绍了当前的机会，开放挑战和预期的未来趋势。

基于我们对红外目标的观察，沿着序列帧内的严重变化很高。在本文中，我们提出了一种动态的重新参数化网络（DRPN）来处理规模变化并平衡红外数据集中的小目标和大目标之间的检测精度。 DRPN采用不同尺寸的卷积内核和动态卷积策略的多个分支。具有不同尺寸卷积粒的多个分支有不同的接收领域大小。动态卷积策略使DRPN自适应重量多个分支。 DRPN可以根据目标的比例变化动态调整接收领域。此外，为了在测试阶段保持有效推断，在训练后通过重新参数化技术进一步将多分支结构转换为单分支结构。关于FLIR，KAIST和INFRAPLANE数据集的广泛实验证明了我们提出的DRPN的有效性。实验结果表明，使用所提出的DRPN作为基本结构而不是SKNET或TridentNET获得了最佳性能的探测器。

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.