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20212026
most citedPixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation

8 citations · 11 across the 12 of their papers we have counts for

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cs.CV2022

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Tobias Riedlinger, Marius Schubert, Karsten Kahl +2

Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and…

cs.CV2022

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning

Julian Burghoff, Robin Chan, Hanno Gottschalk +4

Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertai…