@Article{WenjuanGong2023,
author="Wenjuan Gong
and Yue Zhang
and Wei Wang
and Peng Cheng
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="2023",
volume="20",
number="2",
pages="1--20",
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",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3862), last updated on Fri, 12 Jan 2024 16:10:46 +0100",
opturl="https://doi.org/10.1145/3539576"
}