2022-09-09
尽管神经网络在计算机视觉任务中取得了成功，但数字“神经元”还是生物神经元的非常松散的近似。当今的学习方法旨在在具有数字数据表示（例如图像帧）的数字设备上运行。相比之下，生物视觉系统通常比最先进的数字计算机视觉算法更有能力和高效。事件摄像机是一种新兴的传感器技术，它以异步射击像素模仿生物学视觉，避免了图像框架的概念。为了利用现代学习技术，许多基于事件的算法被迫将事件累积回图像帧，在某种程度上浪费了事件摄像机的优势。我们遵循相反的范式，并开发一种新型的神经网络，该网络更接近原始事件数据流。我们证明了角速度回归和竞争性光流估计中的最新性能，同时避免了与训练SNN相关的困难。此外，我们所提出的方法的处理延迟小于1/10，而连续推断将这种改进增加了另一个数量级。
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本文提出了一个开放而全面的框架，以系统地评估对自我监督单眼估计的最新贡献。这包括训练，骨干，建筑设计选择和损失功能。该领域的许多论文在建筑设计或损失配方中宣称新颖性。但是，简单地更新历史系统的骨干会导致25％的相对改善，从而使其胜过大多数现有系统。对该领域论文的系统评估并不直接。在以前的论文中比较类似于类似的需要，这意味着评估协议中的长期错误在现场无处不在。许多论文可能不仅针对特定数据集进行了优化，而且还针对数据和评估标准的错误。为了帮助该领域的未来研究，我们发布了模块化代码库，可以轻松评估针对校正的数据和评估标准的替代设计决策。我们重新实施，验证和重新评估16个最先进的贡献，并引入一个新的数据集（SYNS-Patches），其中包含各种自然和城市场景中的密集室外深度地图。这允许计算复杂区域（例如深度边界）的信息指标。
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扫描像素摄像机是一种新型的低成本低功率传感器，不受衍射限制。它作为扫描过程中从场景的各个部分提取的样品序列产生数据。它可以提供非常详细的图像，而牺牲了采样和缓慢的图像获取时间。本文提出了一种新的算法，该算法允许传感器在此序列的过程中调整采样量。这可以通过最大程度地减少图像和传输场景所需的带宽和时间来克服这些限制，同时保持图像质量。我们检查了图像分类和语义分割的应用，与完全采样的输入相比，能够获得相似的结果，而使用样本少80％
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在现代机器学习研究中，概括到以前看不见的任务的能力几乎是一个关键的挑战。它也是未来“将军AI”的基石。任何部署在现实世界应用中的人为智能代理，都必须随时适应未知环境。研究人员通常依靠强化和模仿学习来通过试用和错误学习来在线适应新任务。但是，这对于需要许多时间段或大量子任务才能完成的复杂任务可能具有挑战性。这些“长范围”任务遭受了样本效率低下的损失，并且可能需要非常长的培训时间，然后代理人才能学习执行必要的长期计划。在这项工作中，我们介绍了案例，该案例试图通过使用自适应“不久的将来”子目标训练模仿学习代理来解决这些问题。这些子观念在每个步骤中使用构图算术在学习潜在的表示空间中进行重新计算。除了提高标准长期任务的学习效率外，这种方法还可以使对以前看不见的任务进行一次性的概括，只有在不同环境中为该任务进行单个参考轨迹。我们的实验表明，所提出的方法始终优于先前的最新成分模仿学习方法30％。
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在这项工作中，我们介绍了一种新的观点，用于在多任务模仿学习中学习可转移的内容。人类能够转移技能和知识。如果我们可以骑自行车工作并开车去商店，我们还可以骑自行车去商店并开车去上班。我们从中汲取灵感，假设策略网络的潜在记忆可以分为两个分区。这些要么包含有关任务的环境环境的知识，要么包含解决任务所需的可推广技能。这可以提高培训效率，并更好地概括相同环境中的技能和在看不见的环境中的同一任务。我们使用了建议的方法来训练两个不同的多任务IL环境的分解代理。在这两种情况下，我们的任务成功率都超过了SOTA的30％。我们还向真正的机器人进行导航证明了这一点。
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避免障碍的广泛范围导致了许多基于计算机视觉的方法。尽管受欢迎，但这不是一个解决问题。使用相机和深度传感器的传统计算机视觉技术通常专注于静态场景，或依赖于障碍物的前沿。生物启发传感器的最新发展将事件相机作为动态场景的引人注目的选择。尽管这些传感器的基于帧的对应物具有许多优点，但是高动态范围和时间分辨率，因此基于事件的感知在很大程度上存在于2D中。这通常导致解决方案依赖于启发式和特定于特定任务。我们表明，在执行障碍物避免时，事件和深度的融合克服了每个单独的模型的故障情况。我们所提出的方法统一事件摄像机和LIDAR流，以估计未经现场几何或障碍物的先验知识的度量对抗。此外，我们还发布了一个基于事件的基于事件的数据集，具有超过700个扫描场景的六个可视流。
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New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.
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Tobacco origin identification is significantly important in tobacco industry. Modeling analysis for sensor data with near infrared spectroscopy has become a popular method for rapid detection of internal features. However, for sensor data analysis using traditional artificial neural network or deep network models, the training process is extremely time-consuming. In this paper, a novel broad learning system with Takagi-Sugeno (TS) fuzzy subsystem is proposed for rapid identification of tobacco origin. Incremental learning is employed in the proposed method, which obtains the weight matrix of the network after a very small amount of computation, resulting in much shorter training time for the model, with only about 3 seconds for the extra step training. The experimental results show that the TS fuzzy subsystem can extract features from the near infrared data and effectively improve the recognition performance. The proposed method can achieve the highest prediction accuracy (95.59 %) in comparison to the traditional classification algorithms, artificial neural network, and deep convolutional neural network, and has a great advantage in the training time with only about 128 seconds.
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Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.
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As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.
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