@Article{WenjuanGong2022,
author="Wenjuan Gong
and Zhang Yue
and Wei Wang
and Cheng Peng
and Jordi Gonzalez",
title="Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition",
journal="ACM Transactions on Multimedia Computing, Communications, and Applications",
year="2022",
optkeywords="Feature Fusion",
optkeywords="Model Fusion",
optkeywords="Meta-Learning",
optkeywords="Micro-Expression Recognition",
abstract="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.",
optnote="ISE; 600.157",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3692), last updated on Tue, 25 Apr 2023 15:36:06 +0200",
doi="10.1145/3539576"
}