智能城市应用程序（例如智能交通路由或事故预防）依赖计算机视觉方法来确切的车辆定位和跟踪。由于精确标记的数据缺乏，从多个摄像机中检测和跟踪3D的车辆被证明是探索挑战的。我们提出了一个庞大的合成数据集，用于多个重叠和非重叠摄像头视图中的多个车辆跟踪和分割。与现有的数据集不同，该数据集仅为2D边界框提供跟踪地面真实，我们的数据集还包含适用于相机和世界坐标中的3D边界框的完美标签，深度估计以及实例，语义和泛型细分。该数据集由17个小时的标记视频材料组成，从64个不同的一天，雨，黎明和夜幕播放的340张摄像机录制，使其成为迄今为止多目标多型多相机跟踪的最广泛数据集。我们提供用于检测，车辆重新识别以及单摄像机跟踪的基准。代码和数据公开可用。

图像分辨率或一般图像质量在当今面部识别系统的性能中起着至关重要的作用。为了解决这个问题，我们提出了一种流行的三胞胎损失的新型组合，以通过微调现有面部识别模型来提高与图像分辨率的鲁棒性。随着八度损失，我们利用高分辨率图像及其合成下采样变体之间的关系与其身份标签共同采样。通过我们的方法对几种最先进的方法进行微调证明，我们可以在各种数据集上显着提高跨分辨率（高低分辨率）面部验证的性能，而不会有意义地加剧高高度的性能分辨率图像。我们的方法应用于FaceTransFormer网络，在挑战性的XQLFW数据集上达到95.12％的面对验证精度，同时在LFW数据库上达到99.73％。此外，低到低面验证精度从我们的方法中受益。我们发布我们的代码，以允许将OCTUPLET损失的无缝集成到现有框架中。

对抗训练方法是针对对抗性例子的最先进（SOTA）经验防御方法。事实证明，许多正则化方法与对抗训练的组合有效。然而，这种正则化方法是在时域中实现的。由于对抗性脆弱性可以被视为一种高频现象，因此必须调节频域中的对抗训练的神经网络模型。面对这些挑战，我们对小波的正则化属性进行了理论分析，可以增强对抗性训练。我们提出了一种基于HAAR小波分解的小波正则化方法，该方法称为小波平均池。该小波正则化模块集成到宽的残留神经网络中，因此形成了新的WideWavelEtResnet模型。在CIFAR-10和CIFAR-100的数据集上，我们提出的对抗小波训练方法在不同类型的攻击下实现了相当大的鲁棒性。它验证了以下假设：我们的小波正则化方法可以增强对抗性的鲁棒性，尤其是在深宽的神经网络中。实施了频率原理（F原理）和解释性的可视化实验，以显示我们方法的有效性。提出了基于不同小波碱函数的详细比较。该代码可在存储库中获得：\ url {https://github.com/momo1986/AdversarialWavelTraining}。

无数应用程序取决于具有现代物体探测器的可靠置信度估计的准确预测。然而，众所周知，包括对象探测器的神经网络产生错误的置换置信度估计。最近的工作甚至表明，探测器的置信度预测是关于对象大小和位置的偏置，但仍然尚不清楚该偏差如何涉及受影响的对象检测器的性能。我们正式证明，条件置信度偏差损害了对象探测器的预期性能，并经验验证这些发现。具体而言，我们演示了如何修改直方图融合校准，不仅避免性能障碍，而且还通过条件置信度校准提高性能。我们进一步发现，在探测器的训练数据上产生的检测中也存在置信度偏差，我们利用在不使用其他数据的情况下执行我们的去偏置。此外，测试时间增强放大了这种偏差，从我们的校准方法产生了更大的性能。最后，我们在不同的对象检测架构上验证了我们的调查结果，并在没有额外数据或培训的情况下显示最多0.6张地图和0.8 MAP50的改进。

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems. The objective of this article is to introduce practitioners and researchers from the field of Information Systems (IS) to data-centric AI. We define relevant terms, provide key characteristics to contrast the data-centric paradigm to the model-centric one, and introduce a framework for data-centric AI. We distinguish data-centric AI from related concepts and discuss its longer-term implications for the IS community.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

This paper describes several improvements to a new method for signal decomposition that we recently formulated under the name of Differentiable Dictionary Search (DDS). The fundamental idea of DDS is to exploit a class of powerful deep invertible density estimators called normalizing flows, to model the dictionary in a linear decomposition method such as NMF, effectively creating a bijection between the space of dictionary elements and the associated probability space, allowing a differentiable search through the dictionary space, guided by the estimated densities. As the initial formulation was a proof of concept with some practical limitations, we will present several steps towards making it scalable, hoping to improve both the computational complexity of the method and its signal decomposition capabilities. As a testbed for experimental evaluation, we choose the task of frame-level piano transcription, where the signal is to be decomposed into sources whose activity is attributed to individual piano notes. To highlight the impact of improved non-linear modelling of sources, we compare variants of our method to a linear overcomplete NMF baseline. Experimental results will show that even in the absence of additional constraints, our models produce increasingly sparse and precise decompositions, according to two pertinent evaluation measures.

We introduce a novel way to incorporate prior information into (semi-) supervised non-negative matrix factorization, which we call differentiable dictionary search. It enables general, highly flexible and principled modelling of mixtures where non-linear sources are linearly mixed. We study its behavior on an audio decomposition task, and conduct an extensive, highly controlled study of its modelling capabilities.

Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We propose a training procedure for efficient CNNs based on offline Knowledge Distillation (KD) from high-performing yet complex transformers. The proposed training schema and the efficient CNN design based on MobileNetV3 results in models outperforming previous solutions in terms of parameter and computational efficiency and prediction performance. We provide models of different complexity levels, scaling from low-complexity models up to a new state-of-the-art performance of .483 mAP on AudioSet. Source Code available at: https://github.com/fschmid56/EfficientAT

Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.