1 code implementation • CVPR 2024 • Yuting Li, Yingyi Chen, Xuanlong Yu, Dexiong Chen, Xi Shen
In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability.
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1 code implementation • 27 Sep 2023 • Gianni Franchi, Marwane Hariat, Xuanlong Yu, Nacim Belkhir, Antoine Manzanera, David Filliat
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes.
no code implementations • 27 Sep 2023 • Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo, Hanlin Tian, Kenta Matsui, Tianhao Wang, Fahmy Adan, Zhitong Gao, Xuming He, Quentin Bouniot, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill D. F. Campbell, Gianni Franchi
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
1 code implementation • 17 Aug 2023 • Xuanlong Yu, Gianni Franchi, Jindong Gu, Emanuel Aldea
In this work, we propose a generalized AuxUE scheme for more robust uncertainty quantification on regression tasks.
1 code implementation • 20 Jul 2022 • Gianni Franchi, Xuanlong Yu, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, David Filliat
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems.
3 code implementations • 2 Mar 2022 • Gianni Franchi, Xuanlong Yu, Andrei Bursuc, Angel Tena, Rémi Kazmierczak, Séverine Dubuisson, Emanuel Aldea, David Filliat
However, disentangling the different types and sources of uncertainty is non trivial for most datasets, especially since there is no ground truth for uncertainty.
no code implementations • 24 Feb 2022 • Xuanlong Yu, Gianni Franchi, Emanuel Aldea
To this end, this paper will introduce a taxonomy and summary of CAR approaches, a new uncertainty estimation solution for CAR, and a set of experiments on depth accuracy and uncertainty quantification for CAR-based models on KITTI dataset.
1 code implementation • 21 Oct 2021 • Xuanlong Yu, Gianni Franchi, Emanuel Aldea
It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results.