Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation

16 February 2024

Papers citing "Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation"

8 / 8 papers shown

Title
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Alessio Quercia Erenus Yildiz Zhuo Cao Kai Krajsek Abigail Morrison Ira Assent Hanno Scharr 56 0 0 22 Jan 2025
Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration D. Wolf Alexander Braun Markus Ulrich 89 0 0 18 Dec 2024
UNIC: Universal Classification Models via Multi-teacher Distillation Mert Bulent Sariyildiz Philippe Weinzaepfel Thomas Lucas Diane Larlus Yannis Kalantidis 37 6 0 09 Aug 2024
Density Uncertainty Quantification with NeRF-Ensembles: Impact of Data and Scene Constraints M. Jäger Steven Landgraf B. Jutzi 32 2 0 22 Dec 2023
Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition Didan Deng Liang Wu Bertram E. Shi 46 32 0 21 Jul 2021
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images Lei He Jiwen Lu Guanghui Wang Shiyu Song Jie Zhou 44 69 0 19 Jan 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015