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Improving the Reliability for Confidence Estimation. / Qu, Haoxuan; Li, Yanchao; Foo, Lin Geng et al.
Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. ed. / Shai Avidan; Gabriel Brostow; Moustapha Cissé; Giovanni Maria Farinella; Tal Hassner. Cham: Springer Science and Business Media Deutschland GmbH, 2022. p. 391-408 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13687 LNCS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Qu, H, Li, Y, Foo, LG, Kuen, J, Gu, J
& Liu, J 2022,
Improving the Reliability for Confidence Estimation. in S Avidan, G Brostow, M Cissé, GM Farinella & T Hassner (eds),
Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13687 LNCS, Springer Science and Business Media Deutschland GmbH, Cham, pp. 391-408, 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel,
23/10/22.
https://doi.org/10.1007/978-3-031-19812-0_23APA
Qu, H., Li, Y., Foo, L. G., Kuen, J., Gu, J.
, & Liu, J. (2022).
Improving the Reliability for Confidence Estimation. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.),
Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings (pp. 391-408). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13687 LNCS). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-19812-0_23Vancouver
Qu H, Li Y, Foo LG, Kuen J, Gu J
, Liu J.
Improving the Reliability for Confidence Estimation. In Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T, editors, Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. Cham: Springer Science and Business Media Deutschland GmbH. 2022. p. 391-408. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19812-0_23
Author
Qu, Haoxuan ; Li, Yanchao ; Foo, Lin Geng et al. /
Improving the Reliability for Confidence Estimation. Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. editor / Shai Avidan ; Gabriel Brostow ; Moustapha Cissé ; Giovanni Maria Farinella ; Tal Hassner. Cham : Springer Science and Business Media Deutschland GmbH, 2022. pp. 391-408 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{9993e8a2544e43bbaaa8819565c23ca3,
title = "Improving the Reliability for Confidence Estimation",
abstract = "Confidence estimation, a task that aims to evaluate the trustworthiness of the model{\textquoteright}s prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification.",
keywords = "Confidence estimation, Meta-learning",
author = "Haoxuan Qu and Yanchao Li and Foo, {Lin Geng} and Jason Kuen and Jiuxiang Gu and Jun Liu",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th European Conference on Computer Vision, ECCV 2022 ; Conference date: 23-10-2022 Through 27-10-2022",
year = "2022",
month = oct,
day = "30",
doi = "10.1007/978-3-031-19812-0_23",
language = "English",
isbn = "9783031198113",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "391--408",
editor = "Shai Avidan and Gabriel Brostow and Moustapha Ciss{\'e} and Farinella, {Giovanni Maria} and Tal Hassner",
booktitle = "Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Improving the Reliability for Confidence Estimation
AU - Qu, Haoxuan
AU - Li, Yanchao
AU - Foo, Lin Geng
AU - Kuen, Jason
AU - Gu, Jiuxiang
AU - Liu, Jun
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/10/30
Y1 - 2022/10/30
N2 - Confidence estimation, a task that aims to evaluate the trustworthiness of the model’s prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification.
AB - Confidence estimation, a task that aims to evaluate the trustworthiness of the model’s prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification.
KW - Confidence estimation
KW - Meta-learning
U2 - 10.1007/978-3-031-19812-0_23
DO - 10.1007/978-3-031-19812-0_23
M3 - Conference contribution/Paper
AN - SCOPUS:85142705587
SN - 9783031198113
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 391
EP - 408
BT - Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
A2 - Avidan, Shai
A2 - Brostow, Gabriel
A2 - Cissé, Moustapha
A2 - Farinella, Giovanni Maria
A2 - Hassner, Tal
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 17th European Conference on Computer Vision, ECCV 2022
Y2 - 23 October 2022 through 27 October 2022
ER -
