Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3D reconstruction using only 5-9 images of an object.

获取3D对象表示对于创建照片现实的模拟器和为AR/VR应用程序收集资产很重要。神经领域已经显示出其在学习2D图像的场景的连续体积表示方面的有效性，但是从这些模型中获取对象表示，并以较弱的监督仍然是一个开放的挑战。在本文中，我们介绍了Laterf，一种从给定的2D图像和已知相机姿势的2D图像中提取感兴趣对象的方法，对象的自然语言描述以及少数对象和非对象标签 - 输入图像中的对象点。为了忠实地从场景中提取对象，后来在每个3D点上都以其他“对象”概率扩展NERF公式。此外，我们利用预先训练的剪辑模型与我们可区分的对象渲染器相结合的丰富潜在空间来注入对象的封闭部分。我们在合成数据集和真实数据集上展示了高保真对象提取，并通过广泛的消融研究证明我们的设计选择是合理的。

数据驱动的模拟器承诺高数据效率进行驾驶策略学习。当用于建模相互作用时，这种数据效率变为瓶颈：小型基础数据集通常缺乏用于学习交互式驾驶的有趣和具有挑战性的边缘案例。我们通过提出使用绘制的ADO车辆学习强大的驾驶策略的仿真方法来解决这一挑战。因此，我们的方法可用于学习涉及多代理交互的策略，并允许通过最先进的策略学习方法进行培训。我们评估了驾驶中学习标准交互情景的方法。在广泛的实验中，我们的工作表明，由此产生的政策可以直接转移到全规模的自治车辆，而无需使用任何传统的SIM-to-Real传输技术，例如域随机化。

仿真有可能改变在安全关键方案中部署的移动代理的强大算法的开发。然而，对现有模拟发动机的差的光敏性和缺乏不同的传感器方式保持关键障碍朝来实现这种潜力。在这里，我们呈现Vista，一个开源，数据驱动模拟器，用于为自动车辆集成多种类型的传感器。使用高保真度，实际数据集，Vista表示和模拟RGB摄像机，3D LIDAR和基于事件的相机，可以快速生成模拟中的新颖观点，从而富集可用于与难以实现的拐角案例的政策学习的数据在物理世界中捕获。使用Vista，我们展示了在每个传感器类型上培训和测试对控制策略的能力，并通过在全尺度自主车辆上进行展示这种方法的功率。在Vista中学到的政策展示了SIM-TEAR-REAL转移，而不是改进和更高的鲁棒性，而不是完全在现实世界数据上培训的鲁棒性。

连续控制的强化学习（RL）通常采用其支持涵盖整个动作空间的分布。在这项工作中，我们调查了培训的代理经常更喜欢在该空间的界限中普遍采取行动的俗称已知的现象。我们在最佳控制中汲取理论联系，以发出Bang-Bang行为的出现，并在各种最近的RL算法中提供广泛的实证评估。我们通过伯努利分布替换正常高斯，该分布仅考虑沿着每个动作维度的极端 - Bang-Bang控制器。令人惊讶的是，这在几种连续控制基准测试中实现了最先进的性能 - 与机器人硬件相比，能量和维护成本影响控制器选择。由于勘探，学习和最终解决方案纠缠在RL中，我们提供了额外的模仿学习实验，以减少探索对我们分析的影响。最后，我们表明我们的观察结果概括了旨在模拟现实世界挑战和评估因素来减轻Bang-Bang解决方案的因素的环境。我们的调查结果强调了对基准测试连续控制算法的挑战，特别是在潜在的现实世界应用中。

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results

The correct functioning of photovoltaic (PV) cells is critical to ensuring the optimal performance of a solar plant. Anomaly detection techniques for PV cells can result in significant cost savings in operation and maintenance (O&M). Recent research has focused on deep learning techniques for automatically detecting anomalies in Electroluminescence (EL) images. Automated anomaly annotations can improve current O&M methodologies and help develop decision-making systems to extend the life-cycle of the PV cells and predict failures. This paper addresses the lack of anomaly segmentation annotations in the literature by proposing a combination of state-of-the-art data-driven techniques to create a Golden Standard benchmark. The proposed method stands out for (1) its adaptability to new PV cell types, (2) cost-efficient fine-tuning, and (3) leverage public datasets to generate advanced annotations. The methodology has been validated in the annotation of a widely used dataset, obtaining a reduction of the annotation cost by 60%.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.