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pro vyhledávání: '"Briot, Alexandre"'
Recent works on predictive uncertainty estimation have shown promising results on Out-Of-Distribution (OOD) detection for semantic segmentation. However, these methods struggle to precisely locate the point of interest in the image, i.e, the anomaly.
Externí odkaz:
http://arxiv.org/abs/2207.08782
In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate or fast but not both which limits their usability in real world appli
Externí odkaz:
http://arxiv.org/abs/2108.01634
In this paper, we show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving, by triggering a fallback behavior if a target accuracy cannot be guaranteed. We introduce a new uncertainty measure
Externí odkaz:
http://arxiv.org/abs/2105.13688
State of the art Deep Neural Networks (DNN) can now achieve above human level accuracy on image classification tasks. However their outstanding performances come along with a complex inference mechanism making them arduously interpretable models. In
Externí odkaz:
http://arxiv.org/abs/1910.00387