用于对象检测的注释边界框很昂贵，耗时且容易出错。在这项工作中，我们提出了一个基于DITR的框架，该框架旨在在部分注释的密集场景数据集中明确完成丢失的注释。这减少了注释场景中的每个对象实例，从而降低注释成本。完成DETR解码器中的对象查询，并使用图像中对象的补丁信息。结合匹配损失，它可以有效地找到与输入补丁相似的对象并完成丢失的注释。我们表明，我们的框架优于最先进的方法，例如软采样和公正的老师，同时可以与这些方法一起使用以进一步提高其性能。我们的框架对下游对象探测器的选择也不可知。我们显示了多个流行探测器的性能改进，例如在多个密集的场景数据集中更快的R-CNN，CASCADE R-CNN，CENTERNET2和可变形的DETR。

我们研究视觉变压器（VIT）的半监督学习（SSL），尽管VIT架构广泛采用了不同的任务，但视觉变形金刚（VIT）还是一个不足的主题。为了解决这个问题，我们提出了一条新的SSL管道，该管道由第一个联合国/自制的预训练组成，然后是监督的微调，最后是半监督的微调。在半监督的微调阶段，我们采用指数的移动平均线（EMA） - 教师框架，而不是流行的FixMatch，因为前者更稳定，并且为半手不见的视觉变压器提供了更高的准确性。此外，我们提出了一种概率的伪混合机制来插入未标记的样品及其伪标签以改善正则化，这对于训练电感偏差较弱的训练VIT很重要。我们所提出的方法被称为半vit，比半监督分类设置中的CNN对应物获得可比性或更好的性能。半vit还享受VIT的可伸缩性优势，可以很容易地扩展到具有越来越高的精度的大型模型。例如，半效率总数仅使用1％标签在Imagenet上获得令人印象深刻的80％TOP-1精度，使用100％ImageNet标签与Inception-V4相当。

在本文中，我们研究了如何在视觉和语言（V+L）表示学习中使用蒙版的信号建模。与其独立开发蒙面语言建模（MLM）和蒙面图像建模（MIM），我们建议建立关节蒙面的视觉和语言建模，其中一种模态的掩盖信号是在另一种方式的帮助下重建的。这是由图像文本配对数据的性质和文本传达几乎相同的信息但以不同格式传达的。在另一种模态下进行的一种模式的掩盖信号重建也可以隐式学习语言令牌和图像贴片之间的跨模式对齐。我们对各种V+L任务的实验表明，该建议的方法不仅可以通过使用大量数据来实现最先进的性能，而且还可以通过有限的培训数据的制度优于其他竞争对手。

大多数现有的作品在少数拍摄对象检测（FSOD）上的工作重点是从类似域中进行预训练和几乎没有弹出的学习数据集的设置。但是，在多个域中，很少有射击算法很重要。因此，评估需要反映广泛的应用。我们提出了一个多域数少数对象检测（MOFSOD）基准，该基准由来自各个域的10个数据集组成，以评估FSOD算法。我们全面分析了冷冻层，不同的体系结构和不同的预训练数据集对FSOD性能的影响。我们的经验结果表明，以前的作品中尚未探索过的几个关键因素：1）与以前的信念相反，在多域基准测试中，微调（FT）是FSOD的强大基线，在PAR上表现或更好最先进的（SOTA）算法； 2）利用FT作为基线使我们能够探索多个体系结构，我们发现它们对下游的几杆任务产生重大影响，即使具有类似的训练性能； 3）通过取消预训练和几乎没有学习的学习，MOFSOD使我们能够探索不同的预训练数据集的影响，并且正确的选择可以显着提高下游任务的性能。基于这些发现，我们列出了可能提高FSOD性能的调查途径，并对现有算法进行了两次简单修改，这些算法导致MOFSOD基准上的SOTA性能。该代码可在https://github.com/amazon-research/few-shot-object-detection-benchmark上获得。

在从少数类（基类）开始的情况下，已经广泛研究了课堂学习学习（CIL）。取而代之的是，我们探索了一个研究不足的CIL现实环境，该设置是从在大量基类中进行预训练的强大模型开始。我们假设强大的基本模型可以为新颖的类别提供良好的表示，并且可以通过小型适应来进行渐进的学习。我们提出了一个2阶段的训练方案，i）功能增强 - 将部分的克隆部分克隆并在新型数据上进行微调，ii）融合 - 将基础和新型分类器组合到统一的分类器中。实验表明，所提出的方法在大型成像网数据集上的最先进的CIL方法明显优于最先进的CIL方法（例如，总体准确度 +最佳 +最佳精度为10％）。我们还建议和分析研究研究的实际CIL方案，例如与分布转移的基础新颖性重叠。我们提出的方法是鲁棒的，并概括了所有分析的CIL设置。代码可从https://github.com/amazon-research/sp-cil获得。

我们介绍了一种计算关于数据集的学习任务的导数的方法。学习任务是从训练设置到验证错误的函数，可以由培训的深神经网络（DNN）表示。 “数据集导数”是一个线性运算符，围绕培训的模型计算，它通知每个训练样本的权重的扰动如何影响验证误差，通常在单独的验证数据集上计算。我们的方法，DIVA（可微分验证）铰接在预先训练的DNN周围的休假交叉验证误差的闭合形式微分表达上。这种表达构成数据集衍生物。 Diva可用于数据集自动策策，例如用错误的注释删除样本，使用其他相关样本增强数据集或重新平衡。更一般地，DIVA可用于优化数据集，以及模型的参数，作为培训过程的一部分，而无需单独的验证数据集，与AutomL的双层优化方法不同。为了说明DIVA的灵活性，我们向样本自动策展任务报告实验，如异常值拒绝，数据集扩展和多模态数据的自动聚合。

Recently, e-scooter-involved crashes have increased significantly but little information is available about the behaviors of on-road e-scooter riders. Most existing e-scooter crash research was based on retrospectively descriptive media reports, emergency room patient records, and crash reports. This paper presents a naturalistic driving study with a focus on e-scooter and vehicle encounters. The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms. The data was collected using an instrumented vehicle and an e-scooter rider wearable system, respectively. A three-step data analysis process is developed. First, semi-automatic data labeling extracts e-scooter rider images and non-rider human images in similar environments to train an e-scooter-rider classifier. Then, a multi-step scene reconstruction pipeline generates vehicle and e-scooter trajectories in all encounters. The final step is to model e-scooter rider behaviors and e-scooter-vehicle encounter scenarios. A total of 500 vehicle to e-scooter interactions are analyzed. The variables pertaining to the same are also discussed in this paper.

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data.

In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20--40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023.

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.