Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit

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rcGAN: Learning a Generative Model for Arbitrary Size Image Generationage used to train our model. Our two-steps method uses a randomly conditioned convolutional generative adversarial network (rcGAN) trained on patches obtained from a reference image. It can capture the reference image internal patches distribution and then produce high-quality samples that share wit

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Sketch-Inspector: A Deep Mixture Model for High-Quality Sketch Generation of Catsen made in previous studies in this area, a relatively high proportion of the generated figures are too abstract to recognize, which illustrates that AIs fail to learn the general pattern of the target object when drawing. This paper posits that supervising the process of stroke generation can lead

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Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3n solutions. However, these algorithms often impose prohibitive levels of memory and computational overhead, especially in resource-constrained environments. In this study, we combine the state-of-the-art object-detection model YOLOv3 with depthwise separable convolutions and variational dropout in

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Uncertainty Estimates in Deep Generative Models Using Gaussian Processesliability of the outcome of machine learning systems. Gaussian processes are widely known as a method in machine learning which provides estimates of uncertainty. Moreover, Gaussian processes have been shown to be equivalent to deep neural networks with infinitely wide layers. This equivalence sugge

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Towards Optimal Ship Navigation Using Image Processing Plotting Aid (ARPA) and Electronic Chart Display and Information System (ECDIS). Location map, marine traffic, geographical conditions, and obstacles in a region can be monitored by these technologies. The obstacles may vary from icebergs and ice blocks to islands, debris, rocks, or other vessels i

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Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Conto approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and prefabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep le

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