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Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout
  for Landmark-based Facial Expression Recognition with Uncertainty Estimation

Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation

8 June 2021
Negar Heidari
Alexandros Iosifidis
    3DH
    CVBM
ArXivPDFHTML

Papers citing "Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation"

2 / 2 papers shown
Title
Learning Diversified Feature Representations for Facial Expression
  Recognition in the Wild
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
Negar Heidari
Alexandros Iosifidis
CVBM
26
3
0
17 Oct 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
1