We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model builds long-range video features by learning from sets of video clip-level features extracted with a pre-trained backbone. To train the model, we propose a self-supervised objective consisting of masked clip feature prediction. We apply sparsity to both the input, by extracting a random set of video clips, and to the loss function, by only reconstructing the sparse inputs. Moreover, we use dimensionality reduction by working in the latent space of a pre-trained backbone applied to single video clips. The video representation is then obtained by taking the ensemble of the concatenation of embeddings of separate video clips with a video clip set summarization token. These techniques make our method not only extremely efficient to train, but also highly effective in transfer learning. We demonstrate that our video representation yields state-of-the-art performance with linear, non-linear, and $k$-NN probing on common action classification datasets.

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.

我们提出Simprov-可扩展的图像出处框架，将查询图像匹配回到可信的原始数据库，并在查询上确定可能的操作。 Simprov由三个阶段组成：检索Top-K最相似图像的可扩展搜索阶段；一个重新排列和近乎解复的检测阶段，用于识别候选人之间的原件；最后，在查询中定位区域的操纵检测和可视化阶段可能被操纵与原始区域不同。 Simprov对在线再分配过程中通常发生的良性图像转换非常强大，例如由于噪声和重新压缩降解而引起的工件，以及由于图像填充，翘曲，尺寸和形状的变化而引起的过度转换。通过对比较器体系结构中可区分的翘曲模块的端到端训练，可以实现对实地转换的鲁棒性。我们证明了对1亿张图像的数据集的有效检索和操纵检测。

我们提出了一种解决从脸部单个运动模糊图像的新观点渲染夏普视频的新颖任务。我们的方法通过隐式地通过三个大型数据集的联合训练来处理面部的几何和运动来处理面部模糊的复杂性：FFHQ和300VW，我们构建的新伯尔尼多视图DataSet（BMFD） 。前两个数据集提供了各种各样的面，并允许我们的模型更好地概括。 BMFD允许我们引入多视图约束，这对于从新的相机视图综合夏普视频至关重要。它由来自多个主题的多种视图的高帧速率同步视频组成，这些拍摄对象的多个观点显示了广泛的面部表情。我们使用高帧率视频通过平均来模拟现实运动模糊。感谢此数据集，我们训练一个神经网络，从单个图像和相应的面凝视中重建3D视频表示。然后，我们将相对于估计的凝视和模糊图像提供相机视点，作为对编码器解码器网络的输入，以生成具有新颖的相机视点的锐框的视频。我们在我们的多视图数据集和Vidtimit的测试对象上展示了我们的方法。

我们介绍了一种新颖的自我监督的对比学习方法，以了解来自未标记视频的表示。现有方法忽略了输入失真的细节，例如，通过学习与时间转换的不变性。相反，我们认为视频表示应该保留视频动态并反映输入的时间操纵。因此，我们利用新的约束来构建对时间转换和更好的捕获视频动态的表示表示。在我们的方法中，视频的增强剪辑之间的相对时间转换被编码在向量中并与其他转换向量形成对比。为了支持时间的设备，我们还提出了将视频的两个剪辑的自我监督分类为1.重叠2.订购或3.无序。我们的实验表明，时代的表示达到最先进的结果，导致UCF101，HMDB51和潜水48上的视频检索和动作识别基准。

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.

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.

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.

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.

Two approaches to AI, neural networks and symbolic systems, have been proven very successful for an array of AI problems. However, neither has been able to achieve the general reasoning ability required for human-like intelligence. It has been argued that this is due to inherent weaknesses in each approach. Luckily, these weaknesses appear to be complementary, with symbolic systems being adept at the kinds of things neural networks have trouble with and vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry by combining neural networks and symbolic AI into integrated systems. Often this has been done by encoding symbolic knowledge into neural networks. Unfortunately, although many different methods for this have been proposed, there is no common definition of an encoding to compare them. We seek to rectify this problem by introducing a semantic framework for neural-symbolic AI, which is then shown to be general enough to account for a large family of neural-symbolic systems. We provide a number of examples and proofs of the application of the framework to the neural encoding of various forms of knowledge representation and neural network. These, at first sight disparate approaches, are all shown to fall within the framework's formal definition of what we call semantic encoding for neural-symbolic AI.