Uncertain facial expression recognition via multi-task assisted correction

Y Liu, X Zhang, J Kauttonen… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Deep models for facial expression recognition achieve high performance by training on
large-scale labeled data. However, publicly available datasets contain uncertain facial …

Low-resolution object recognition with cross-resolution relational contrastive distillation

K Zhang, S Ge, R Shi, D Zeng - IEEE Transactions on Circuits …, 2023‏ - ieeexplore.ieee.org
Recognizing objects in low-resolution images is a challenging task due to the lack of
informative details. Recent studies have shown that knowledge distillation approaches can …

Semi-supervised feature learning for disjoint hyperspectral imagery classification

X Cao, C Li, J Feng, L Jiao - Neurocomputing, 2023‏ - Elsevier
With the introduction of spatial-spectral fusion and deep learning, the classification
performance of hyperspectral imagery (HSI) has been promoted greatly. For some widely …

CalD3r and MenD3s: Spontaneous 3D facial expression databases

L Ulrich, F Marcolin, E Vezzetti, F Nonis… - Journal of Visual …, 2024‏ - Elsevier
In the last couple of decades, the research on 3D facial expression recognition has been
fostered by the creation of tailored databases containing prototypical expressions of different …

From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos

H Li, H Niu, F Zhao - arxiv preprint arxiv:2405.16451, 2024‏ - arxiv.org
Micro-expression recognition (MER) has drawn increasing attention in recent years due to
its potential applications in intelligent medical and lie detection. However, the shortage of …

CSLSEP: an ensemble pruning algorithm based on clustering soft label and sorting for facial expression recognition

S Huang, D Li, Z Zhang, Y Wu, Y Tang, X Chen… - Multimedia Systems, 2023‏ - Springer
Applying ensemble learning to facial expression recognition is an important research field
nowadays, but all may not be better than many, the redundant learners in the classifier pool …

Exploring holistic discriminative representation for micro-expression recognition via contrastive learning

J Zhu, W He, F Wang, H Chang, C Lu, Y Zong - Image and Vision …, 2024‏ - Elsevier
Recently, deep learning-based micro-expression recognition (MER) has been remarkably
successful in the affective computing and computer vision communities. However, the most …

Downstream-pretext domain knowledge traceback for active learning

B Zhang, L Li, ZJ Zha, J Luo… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Active learning (AL) is designed to construct a high-quality labeled dataset by iteratively
selecting the most informative samples. Such sampling heavily relies on data …

Active learning with label quality control

X Wang, X Chi, Y Song, Z Yang - PeerJ Computer Science, 2023‏ - peerj.com
Training deep neural networks requires a large number of labeled samples, which are
typically provided by crowdsourced workers or professionals at a high cost. To obtain …

Decoupling facial motion features and identity features for micro-expression recognition

T **e, G Sun, H Sun, Q Lin, X Ben - PeerJ Computer Science, 2022‏ - peerj.com
Background Micro-expression is a kind of expression produced by people spontaneously
and unconsciously when receiving stimulus. It has the characteristics of low intensity and …