Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.

Automatic labelling of anatomical structures, such as coronary arteries, is critical for diagnosis, yet existing (non-deep learning) methods are limited by a reliance on prior topological knowledge of the expected tree-like structures. As the structure such vascular systems is often difficult to conceptualize, graph-based representations have become popular due to their ability to capture the geometric and topological properties of the morphology in an orientation-independent and abstract manner. However, graph-based learning for automated labeling of tree-like anatomical structures has received limited attention in the literature. The majority of prior studies have limitations in the entity graph construction, are dependent on topological structures, and have limited accuracy due to the anatomical variability between subjects. In this paper, we propose an intuitive graph representation method, well suited to use with 3D coordinate data obtained from angiography scans. We subsequently seek to analyze subject-specific graphs using geometric deep learning. The proposed models leverage expert annotated labels from 141 patients to learn representations of each coronary segment, while capturing the effects of anatomical variability within the training data. We investigate different variants of so-called message passing neural networks. Through extensive evaluations, our pipeline achieves a promising weighted F1-score of 0.805 for labeling coronary artery (13 classes) for a five-fold cross-validation. Considering the ability of graph models in dealing with irregular data, and their scalability for data segmentation, this work highlights the potential of such methods to provide quantitative evidence to support the decisions of medical experts.

机器学习和非接触传感器的进步使您能够在医疗保健环境中理解复杂的人类行为。特别是，已经引入了几种深度学习系统，以实现对自闭症谱系障碍（ASD）等神经发展状况的全面分析。这种情况会影响儿童的早期发育阶段，并且诊断完全依赖于观察孩子的行为和检测行为提示。但是，诊断过程是耗时的，因为它需要长期的行为观察以及专家的稀缺性。我们展示了基于区域的计算机视觉系统的效果，以帮助临床医生和父母分析孩子的行为。为此，我们采用并增强了一个数据集，用于使用在不受控制的环境中捕获的儿童的视频来分析自闭症相关的动作（例如，在各种环境中使用消费级摄像机收集的视频）。通过检测视频中的目标儿童以减少背景噪声的影响，可以预处理数据。在时间卷积模型的有效性的推动下，我们提出了能够从视频帧中提取动作功能并通过分析视频中的框架之间的关系来从视频帧中提取动作功能并分类与自闭症相关的行为。通过对功能提取和学习策略的广泛评估，我们证明了通过膨胀的3D Convnet和多阶段的时间卷积网络实现最佳性能，达到了0.83加权的F1得分，以分类三种自闭症相关的动作，超越表现优于表现现有方法。我们还通过在同一系统中采用ESNET主链来提出一个轻重量解决方案，实现0.71加权F1得分的竞争结果，并在嵌入式系统上实现潜在的部署。

我们向您展示一次（YOCO）进行数据增强。 Yoco将一张图像切成两片，并在每件零件中单独执行数据增强。应用YOCO改善了每个样品的增强的多样性，并鼓励神经网络从部分信息中识别对象。 Yoco享受无参数，轻松使用的属性，并免费提供几乎所有的增强功能。进行了彻底的实验以评估其有效性。我们首先证明Yoco可以无缝地应用于不同的数据增强，神经网络体系结构，并在CIFAR和Imagenet分类任务上带来性能提高，有时会超过传统的图像级增强。此外，我们显示了Yoco益处对比的预培训，以更强大的表示，可以更好地转移到多个下游任务。最后，我们研究了Yoco的许多变体，并经验分析了各个设置的性能。代码可在GitHub上找到。

医疗应用从计算机视觉中的快速进步受益。特别是患者监测，卧床人体姿势估计提供了重要的健康相关指标，具有医学条件评估的潜在价值。尽管该领域的进展巨大，但由于闭塞期间的大量模糊性，并且缺乏用于模型训练的手动标记数据的大型车辆，仍然是一个具有挑战性的任务，特别是具有隐私保留的热红外成像等领域，因此极大的兴趣。通过直接从数据学习功能的自我监督方法的有效性，我们提出了一种多模态条件变形AutoEncoder（MC-VAE），其能够重建在训练期间看到的缺失的模态。这种方法与HRNET一起使用，以使单个模态推断用于床上姿势估计。通过广泛的评估，我们证明身体位置可以从可用的方式得到有效地识别，通过高度依赖于在推理时间访问多种模式的基线模型的PAR结果上实现了PAR结果。拟议的框架支持未来的自我监督学习研究，从单个来源生成强大的模型，并期望它概括了临床环境中的许多未知分布。

荆棘冠的海星（婴儿床）爆发是珊瑚损失的主要原因是巨大的障碍礁（GBR），并且正在进行大量监测和控制计划，以试图管理生态可持续水平的COTS群体。我们释放了GBR上的COTS爆发区域的大规模注释的水下图像数据集，以鼓励机器学习和AI驱动技术的研究，以改善珊瑚礁秤上的COTS群体的检测，监测和管理。该数据集发布并托管在一次竞争中，挑战国际机器学习界，并从这些水下图像中的COTS检测的任务挑战。

现有的基于深度学习的无监督视频对象分割方法仍依靠地面真实的细分面具来训练。在这种情况下令人未知的意味着在推理期间没有使用注释帧。由于获得真实图像场景的地面真实的细分掩码是一种艰苦的任务，我们想到了一个简单的框架，即占主导地位的移动对象分割，既不需要注释数据训练，也不依赖于显着的电视或预先训练的光流程图。灵感来自分层图像表示，我们根据仿射参数运动引入对像素区域进行分组的技术。这使我们的网络能够仅使用RGB图像对为培训和推理的输入来学习主要前景对象的分割。我们使用新的MOVERCARS DataSet为这项新颖任务建立了基线，并对最近的方法表现出竞争性能，这些方法需要培训带有注释面具的最新方法。

我们提出并研究了一个名为“盲图分解”（BID）的新任务，该任务要求将叠加的图像分离为盲点环境中的构成基础图像，也就是说，涉及混合和混合机制的源成分都是未知的。例如，雨水可能由多个组成部分组成，例如雨条，雨滴，雪和阴霾。雨图像可以视为这些组件的任意组合，其中一些或全部。如何将叠加的图像（如多雨图像）分解为不同的源组件是迈向现实世界视觉系统的关键步骤。为了促进对这项新任务的研究，我们构建了多个基准数据集，包括跨多个领域的混合图像分解，实际筛查，以及关节阴影/反射/水印。此外，我们提出了一个简单而通用的盲图分解网络（Biden），以作为未来工作的强大基准。实验结果证明了我们的基准和拜登的有效性。

Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.

State-of-the-art automatic augmentation methods (e.g., AutoAugment and RandAugment) for visual recognition tasks diversify training data using a large set of augmentation operations. The range of magnitudes of many augmentation operations (e.g., brightness and contrast) is continuous. Therefore, to make search computationally tractable, these methods use fixed and manually-defined magnitude ranges for each operation, which may lead to sub-optimal policies. To answer the open question on the importance of magnitude ranges for each augmentation operation, we introduce RangeAugment that allows us to efficiently learn the range of magnitudes for individual as well as composite augmentation operations. RangeAugment uses an auxiliary loss based on image similarity as a measure to control the range of magnitudes of augmentation operations. As a result, RangeAugment has a single scalar parameter for search, image similarity, which we simply optimize via linear search. RangeAugment integrates seamlessly with any model and learns model- and task-specific augmentation policies. With extensive experiments on the ImageNet dataset across different networks, we show that RangeAugment achieves competitive performance to state-of-the-art automatic augmentation methods with 4-5 times fewer augmentation operations. Experimental results on semantic segmentation, object detection, foundation models, and knowledge distillation further shows RangeAugment's effectiveness.