Turning the weights to zero when training a neural network helps in reducing the computational complexity at inference. To progressively increase the sparsity ratio in the network without causing sharp weight discontinuities during training, our work combines soft-thresholding and straight-through gradient estimation to update the raw, i.e. non-thresholded, version of zeroed weights. Our method, named ST-3 for straight-through/soft-thresholding/sparse-training, obtains SoA results, both in terms of accuracy/sparsity and accuracy/FLOPS trade-offs, when progressively increasing the sparsity ratio in a single training cycle. In particular, despite its simplicity, ST-3 favorably compares to the most recent methods, adopting differentiable formulations or bio-inspired neuroregeneration principles. This suggests that the key ingredients for effective sparsification primarily lie in the ability to give the weights the freedom to evolve smoothly across the zero state while progressively increasing the sparsity ratio. Source code and weights available at https://github.com/vanderschuea/stthree

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. However, training a part-based model is a challenging task for two reasons. Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning. Secondly, ReID datasets are not provided with human topographical annotations. In this work, we propose BPBreID, a body part-based ReID model for solving the above issues. We first design two modules for predicting body part attention maps and producing body part-based features of the ReID target. We then propose GiLt, a novel training scheme for learning part-based representations that is robust to occlusions and non-discriminative local appearance. Extensive experiments on popular holistic and occluded datasets show the effectiveness of our proposed method, which outperforms state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the challenging Occluded-Duke dataset. Our code is available at https://github.com/VlSomers/bpbreid.

JPEG-XS为具有限制但合理的位率和低延迟的应用提供了低复杂性图像压缩。我们的论文探讨了JPEG-XS在有损耗的数据包网络上的部署。为了保留低延迟，远期误差校正（FEC）被设想为感兴趣的保护机制。尽管JPEG-XS编码词本质上无法扩展，但我们观察到，编码级数的丢失对解码的图像质量的影响有所不同，具体取决于此编码级数是否对应于CodeStream的标题，以使系数显着性信息，还是对低/高频/高频/高频/高频。数据分别。因此，我们提出了一种最佳的最佳误差保护方案，该方案根据渠道损失率和由代码保护的信息类型来适应芦苇 - 固体代码的冗余水平。我们的实验表明，与没有最佳和最佳保护的传输相比，以5％的损耗率将平均误差降低了92％和65％。

本文提出了一个统一的框架到（i）找到球，（ii）预测姿势，（iii）在团队体育场景中分段播放器的实例掩码。这些问题对自动体育分析，生产和广播有高兴趣。常见做法是通过利用通用最先进的模型，例如Panoptic-Deeblab来单独解决每个问题，用于玩家分割。除了从单任务模型的乘法乘以增加的复杂性之外，由于团队体育场景的复杂性和特异性，使用现成的架子模型也会阻碍性能，如强大的遮挡和运动模糊。为了规避这些限制，我们的论文提出培训一种单一的模型，它通过组合零件强度场和空间嵌入原理来预测球和玩家掩模和姿势。部件强度场提供球和播放器位置，以及播放器接头位置。然后利用空间嵌入来将播放器实例像素联系到其各自的播放器中心，而且还将播放器接头分组成骷髅。我们展示了拟议模型在DeepSport篮球数据集上的有效性，为单独解决每个单独任务的SOA模型实现了可比性的性能。

卷积式自动统计器现在处于图像压缩研究的最前沿。为了改善其熵编码，通常用第二自动码器分析编码器输出以产生每个可变参数化的先前概率分布。相反，我们提出了一种压缩方案，它使用单个卷积的自动化器和多个学习的先前分布作为专家竞争。培训的先前分布存储在累积分布函数的静态表中。在推理期间，该表由熵编码器用作查找表以确定每个空间位置的最佳选择。我们的方法提供了与在其熵编码的一小部分之前的预测参数化获得的速率失真性能，其具有预测的参数化和解码复杂性。

在2015年和2019年之间，地平线的成员2020年资助的创新培训网络名为“Amva4newphysics”，研究了高能量物理问题的先进多变量分析方法和统计学习工具的定制和应用，并开发了完全新的。其中许多方法已成功地用于提高Cern大型Hadron撞机的地图集和CMS实验所执行的数据分析的敏感性;其他几个人，仍然在测试阶段，承诺进一步提高基本物理参数测量的精确度以及新现象的搜索范围。在本文中，在研究和开发的那些中，最相关的新工具以及对其性能的评估。

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

The advances in Artificial Intelligence are creating new opportunities to improve lives of people around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the life of the user directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible i.e., transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model, such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. It is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks. It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems.