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pro vyhledávání: '"Landgraf, Steven"'
Evaluation of Multi-task Uncertainties in Joint Semantic Segmentation and Monocular Depth Estimation
While a number of promising uncertainty quantification methods have been proposed to address the prevailing shortcomings of deep neural networks like overconfidence and lack of explainability, quantifying predictive uncertainties in the context of jo
Externí odkaz:
http://arxiv.org/abs/2405.17097
Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence or lack of explainability and robustness of deep neural networks, albeit one that is often computationally expensive. Many real-world appli
Externí odkaz:
http://arxiv.org/abs/2402.10580
In the fields of computer graphics, computer vision and photogrammetry, Neural Radiance Fields (NeRFs) are a major topic driving current research and development. However, the quality of NeRF-generated 3D scene reconstructions and subsequent surface
Externí odkaz:
http://arxiv.org/abs/2312.14664
Deep neural networks have shown exceptional performance in various tasks, but their lack of robustness, reliability, and tendency to be overconfident pose challenges for their deployment in safety-critical applications like autonomous driving. In thi
Externí odkaz:
http://arxiv.org/abs/2307.09947
In many industrial processes, such as power generation, chemical production, and waste management, accurately monitoring industrial burner flame characteristics is crucial for safe and efficient operation. A key step involves separating the flames fr
Externí odkaz:
http://arxiv.org/abs/2306.14789
Deep neural networks lack interpretability and tend to be overconfident, which poses a serious problem in safety-critical applications like autonomous driving, medical imaging, or machine vision tasks with high demands on reliability. Quantifying the
Externí odkaz:
http://arxiv.org/abs/2303.09843
Akademický článek
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Publikováno v:
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2024, Vol. 10 Issue 2, p129-136, 8p
