在这项工作中，我们为UNET体系结构引入了一个受生物学启发的远程跳过连接，该连接依赖于混合图像的感知幻觉，是同时编码两个图像的图像。早期编码器特征与更深的解码器的融合允许UNET模型产生更细粒度的密集预测。尽管在细分任务中经过证明，但由于这些远程跳过连接还会导致纹理转移伪像，因此网络的好处对于密集的回归任务进行了下降加权。特别是为了深度估计，这损害了光滑度，并引入了假正边，这是由于深度地图的平滑性质而对任务有害的。拟议的Hybridskip连接显示在平衡边缘保存之间的权衡方面的性能得到了改善，以及损害光滑度的纹理转移伪像的最小化。这是通过分别在高频和低频，编码器和解码器特征之间提供的信息的适当和平衡的信息来实现的。

球形摄像机以整体方式捕获场景，并已用于房间布局估计。最近，随着适当数据集的可用性，从单个全向图像中的深度估计也取得了进展。尽管这两个任务是互补的，但很少有作品能够并行探索它们以提高室内几何感知，而那些这样做的人则依靠合成数据或使用过的小型数据集，因为很少有选项可供选择，包括两个布局。在真实场景中的注释和密集的深度图。这部分是由于需要对房间布局进行手动注释。在这项工作中，我们超越了此限制，并生成360几何视觉（360V）数据集，该数据集包括多种模式，多视图立体声数据并自动生成弱布局提示。我们还探索了两个任务之间的明确耦合，以将它们集成到经过单打的训练模型中。我们依靠基于深度的布局重建和基于布局的深度注意，这表明了两项任务的性能提高。通过使用单个360摄像机扫描房间，出现了便利和快速建筑规模3D扫描的机会。

将带家具的房间图像转换为背景的任务 - 仅是非常具有挑战性，因为它需要在仍然保持整体布局和风格的同时进行大量变化。为了获得照片 - 现实和结构一致的背景，现有的深度学习方法使用图像修复方法或将场景布局的学习作为个人任务，以后在不完全可分辨率的语义区域自适应归一代化模块中利用它。为了解决这些缺点，我们将场景布局生成视为特征线性变换问题，并提出了一个简单但有效的调整后的完全可分辨率的软语义区域 - 自适应归一化模块（SoftSean）块。我们展示了现实和深度估计任务的缩短和深度估计任务中的适用性，在那里我们的方法除了减轻培训复杂性和不可差异性问题的优点，超越了定量和定性的比较方法。我们的SoftSean块可用作现有辨别和生成模型的液位模块。在vcl3d.github.io/panodr/上提供实现。

在这项工作中，我们为计算机愿景任务提供了分发换档基准;单眼深度估计。我们的差异化是对野外数据的不受控制测试的更广泛分配转变的分解，以三个不同的分布换档。具体地，我们通过合成生成数据，并分析它们以产生协变量（颜色输入），之前（深度输出）和概念（其关系）分布换档。我们还综合组合并展示了每个对地址的确实是一个不同的挑战，因为堆叠它们产生增加的性能下降，并且不能使用标准方法水平寻址。

Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computing the differentiation in many machine learning and optimisation software tools. Despite the practicality of this technique, the performance of the differentiated programs, especially for functional languages and in the presence of vectors, is suboptimal. We present an AD system for a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed.

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.

The combination of artist-curated scans, and deep implicit functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods are far from perfect. IF-based methods recover free-form geometry but produce disembodied limbs or degenerate shapes for unseen poses or clothes. To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body. What we want is a method that combines the best properties of implicit and explicit methods. To this end, we make two key observations: (1) current networks are better at inferring detailed 2D maps than full-3D surfaces, and (2) a parametric model can be seen as a "canvas" for stitching together detailed surface patches. ECON infers high-fidelity 3D humans even in loose clothes and challenging poses, while having realistic faces and fingers. This goes beyond previous methods. Quantitative, evaluation of the CAPE and Renderpeople datasets shows that ECON is more accurate than the state of the art. Perceptual studies also show that ECON's perceived realism is better by a large margin. Code and models are available for research purposes at https://xiuyuliang.cn/econ

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this conflict, we introduce few-shot learning in order to achieve combustion monitoring and classification for the first time. Two algorithms, Siamese Network coupled with k Nearest Neighbors (SN-kNN) and Prototypical Network (PN), were tested. Rather than utilizing solely visible images as discussed in previous studies, we also used Infrared (IR) images. We analyzed the training process, test performance and inference speed of two algorithms on both image formats, and also used t-SNE to visualize learned features. The results demonstrated that both SN-kNN and PN were capable to distinguish flame states from learning with merely 20 images per flame state. The worst performance, which was realized by PN on IR images, still possessed precision, accuracy, recall, and F1-score above 0.95. We showed that visible images demonstrated more substantial differences between classes and presented more consistent patterns inside the class, which made the training speed and model performance better compared to IR images. In contrast, the relatively low quality of IR images made it difficult for PN to extract distinguishable prototypes, which caused relatively weak performance. With the entrire training set supporting classification, SN-kNN performed well with IR images. On the other hand, benefitting from the architecture design, PN has a much faster speed in training and inference than SN-kNN. The presented work analyzed the characteristics of both algorithms and image formats for the first time, thus providing guidance for their future utilization in combustion monitoring tasks.