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Exploring the Boundaries of Semi-Supervised Facial Expression
  Recognition: Learning from In-Distribution, Out-of-Distribution, and
  Unconstrained Data

Exploring the Boundaries of Semi-Supervised Facial Expression Recognition: Learning from In-Distribution, Out-of-Distribution, and Unconstrained Data

2 June 2023
Shuvendu Roy
Ali Etemad
ArXivPDFHTML

Papers citing "Exploring the Boundaries of Semi-Supervised Facial Expression Recognition: Learning from In-Distribution, Out-of-Distribution, and Unconstrained Data"

3 / 3 papers shown
Title
Face Trees for Expression Recognition
Face Trees for Expression Recognition
Mojtaba Kolahdouzi
Alireza Sepas-Moghaddam
Ali Etemad
CVBM
15
8
0
05 Dec 2021
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
  Labeling
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang
Yidong Wang
Wenxin Hou
Hao Wu
Jindong Wang
Manabu Okumura
T. Shinozaki
AAML
215
861
0
15 Oct 2021
Meta Pseudo Labels
Meta Pseudo Labels
Hieu H. Pham
Zihang Dai
Qizhe Xie
Minh-Thang Luong
Quoc V. Le
VLM
245
655
0
23 Mar 2020
1