自我监督的预制是自然语言处理模型的首选方法，在许多愿景任务中迅速获得普及。最近，自我监督的预借鉴已经显示出胜过许多下游视觉应用的预测，标志着该地区的里程碑。这种优越性归因于传达多个概念的训练图像的不完全标记的负面影响，而是使用单个主要类标签进行注释。虽然自我监督的学习（SSL）原则上没有这种限制，但促进SSL的借口任务的选择是通过向单个概念输出驱动学习过程来实现这种缺点。本研究旨在调查在不使用标签的情况下建模图像中存在的所有概念的可能性。在这方面，所提出的SSL帧工作MC-SSL0.0是迈向多概念自我监督学习（MC-SSL）的步骤，其超出了在图像中建模的单一主导标签，以有效地利用来自所有概念的所有概念在里面。 MC-SSL0.0由两个核心设计概念，组屏蔽模型学习和学习伪概念，用于使用势头（教师学生）框架的数据令牌。多标签和多类图像分类下游任务的实验结果表明，MC-SSL0.0不仅超越了现有的SSL方法，而且超越了监督转移学习。源代码将公开可供社区培训更大的语料库。

随着计算机愿景中变压器架构的普及，研究焦点已转向开发计算有效的设计。基于窗口的本地关注是最近作品采用的主要技术之一。这些方法以非常小的贴片尺寸和小的嵌入尺寸开始，然后执行冲击卷积（贴片合并），以减少特征图尺寸并增加嵌入尺寸，因此，形成像设计的金字塔卷积神经网络（CNN）。在这项工作中，我们通过呈现一种新的各向同性架构，调查变压器中的本地和全球信息建模，以便采用当地窗口和特殊令牌，称为超级令牌，以自我关注。具体地，将单个超级令牌分配给每个图像窗口，该窗口捕获该窗口的丰富本地细节。然后使用这些令牌用于跨窗口通信和全局代表学习。因此，大多数学习都独立于较高层次的图像补丁$（n）$，并且仅基于超级令牌$（n / m ^ 2）$何处，从中学习额外的嵌入量窗口大小。在ImageNet-1K上的标准图像分类中，所提出的基于超代币的变压器（STT-S25）实现了83.5 \％的精度，其等同于带有大约一半参数（49M）的Swin变压器（Swin-B）和推断的两倍时间吞吐量。建议的超级令牌变压器为可视识别任务提供轻量级和有前途的骨干。

跨模式的人重新识别（RE-ID）对于现代视频监视系统至关重要。关键的挑战是与一个人提供的语义信息引起的跨模式表示，并忽略背景信息。这项工作介绍了一种新型的基于卷积神经网络（CNN）的体系结构，旨在学习语义上的跨模式视觉和文本表示。基础构建块，名为Axm-block，是一个统一的多层网络，该网络会动态利用多尺度知识，并根据共享语义重新校准每种模式。为了补充卷积设计，在文本分支中应用上下文注意力以操纵长期依赖性。此外，我们提出了一种独特的设计，以增强基于视觉零件的功能连贯性和局部性信息。我们的框架具有新颖的能力，可以在功能学习阶段隐式学习模式之间的一致语义。统一的特征学习有效地利用文本数据作为视觉表示学习的超级注释信号，并自动拒绝无关的信息。整个AXM-NET经过Cuhk-Pedes数据的端到端训练。我们报告了两个任务的结果，即人搜索和跨模式重新ID。 AXM-NET优于当前最新方法（SOTA）方法，并在Cuhk-Pedes测试集上获得64.44 \％等级@1。在Crossre-ID和Cuhk-Sysu数据集中，它还胜过竞争对手的竞争对手$> $ 10 \％。

Before the transition of AVs to urban roads and subsequently unprecedented changes in traffic conditions, evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a Double/Debiased Machine Learning (DML) model in which the impact of confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature, for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In DML model structure, the standard error term of density parameter is lower than copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, due to the spurious effect of confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.

Neuromorphic vision or event vision is an advanced vision technology, where in contrast to the visible camera that outputs pixels, the event vision generates neuromorphic events every time there is a brightness change which exceeds a specific threshold in the field of view (FOV). This study focuses on leveraging neuromorphic event data for roadside object detection. This is a proof of concept towards building artificial intelligence (AI) based pipelines which can be used for forward perception systems for advanced vehicular applications. The focus is on building efficient state-of-the-art object detection networks with better inference results for fast-moving forward perception using an event camera. In this article, the event-simulated A2D2 dataset is manually annotated and trained on two different YOLOv5 networks (small and large variants). To further assess its robustness, single model testing and ensemble model testing are carried out.

Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.

Unmanned aerial vehicles (UAVs) with on-board cameras are widely used for remote surveillance and video capturing applications. In remote virtual reality (VR) applications, multiple UAVs can be used to capture different partially overlapping angles of the ground target, which can be stitched together to provide 360{\deg} views. This requires coordinated formation of UAVs that is adaptive to movements of the ground target. In this paper, we propose a joint UAV formation and tracking framework to capture 360{\deg} angles of the target. The proposed framework uses a zero touch approach for automated and adaptive reconfiguration of multiple UAVs in a coordinated manner without the need for human intervention. This is suited to both military and civilian applications. Simulation results demonstrate the convergence and configuration of the UAVs with arbitrary initial locations and orientations. The performance has been tested for various number of UAVs and different mobility patterns of the ground target.

从同一场景的单个或多个低分辨率图像中获取高分辨率图像的过程对于现实世界图像和信号处理应用非常感兴趣。这项研究是关于探索基于深度学习的图像超分辨率算法的潜在用法，用于为驾驶汽车内车辆驾驶员监测系统产生高质量的热成像结果。在这项工作中，我们提出并开发了一种新型的多图像超分辨率复发性神经网络，以增强分辨率并提高从未冷却的热摄像机捕获的低分辨率热成像数据的质量。端到端完全卷积神经网络在室内环境条件下从刮擦上训练了30个不同受试者的新获得的热数据。热调谐超分辨率网络的有效性已定量验证，以及在6个不同受试者的测试数据上进行定性验证。该网络能够在验证数据集上达到4倍超分辨率的平均峰信号与噪声比为39.24，在定量和质量上都超过了双色插值。

多语言语音识别已引起大幅关注，作为补偿低资源语言数据稀缺性的有效方法。端到端（E2E）建模比常规混合系统优选，这主要是由于没有词典要求。但是，在有限的数据方案中，混合DNN-HMM仍然优于E2E模型。此外，手动词典创建的问题已通过公开训练的素式训练型（G2P）（G2P）和多种语言的IPA音译来缓解。在本文中，在低资源语言的多语言设置中提出了一种混合DNN-HMM声学模型的新型方法。针对目标语言语言信号的不同单语言模型的后验分布融合在一起。为每个源目标语言对训练了一个单独的回归神经网络，以将后者从源声学模型转换为目标语言。与ASR培训相比，这些网络需要非常有限的数据。与多语言和单语基线相比，后融合的相对增益分别为14.65％和6.5％。跨语性模型融合表明，无需使用依赖语言的ASR的后代，就可以实现可比的结果。

多语言自动语音识别（ASR）系统大多受益于低资源语言，但相对于单语言对应物，多种语言的性能下降。有限的研究集中在理解多语言语音识别设置中的语言行为。在本文中，提出了一种新型的数据驱动方法来研究跨语性的声学表达相似性。该技术衡量了各种单语言模型与目标语音信号的后验分布之间的相似性。深度神经网络被训练为映射网络，以将分布从不同的声学模型转换为直接比较的形式。分析观察到，语言接近性无法通过集合音素的体积真正估计。对拟议的映射网络的熵分析表明，具有较小重叠的语言可以更适合跨语性转移，因此在多语言设置中更有益。最后，提出的后验变换方法被利用为目标语言的单语模型融合。比单语言对应物的相对提高约为8％。