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Found 3 papers, 1 papers with code
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour.
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles
In particular, we are able to show that by leveraging completely unlabeled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labeled data.
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance
To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge.
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