TY  - JOUR
AU  - Wenjuan Gong
AU  - Yue Zhang
AU  - Wei Wang
AU  - Peng Cheng
AU  - Jordi Gonzalez
PY  - 2023//
TI  - Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition
T2  - TMCCA
JO  - ACM Transactions on Multimedia Computing, Communications, and Applications
SP  - 1–20
VL  - 20
IS  - 2
N2  - Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.
UR  - https://doi.org/10.1145/3539576
N1  - ISE
ID  - Wenjuan Gong2023
ER  -