Website|arXiv|BibTeX
Peihao Xiang, Chaohao Lin, Kaida Wu, and Ou Bai
HCPS Laboratory, Department of Electrical and Computer Engineering, Florida International University
Official TensorFlow implementation and pre-trained VideoMAE models for MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition.
Illustration of our MultiMAE-DER.
General Multimodal Model vs. MultiMAE-DER. The uniqueness of our approach lies in the capability to extract features from cross-domain data using only a single encoder, eliminating the need for targeted feature extraction for different modalities.
Multimodal Sequence Fusion Strategies.
The architecture of MultiMAE-DER.
If you have any questions, please feel free to reach me out at pxian001@fiu.edu.
This project is built upon VideoMAE and MAE-DFER. Thanks for their great codebase.
This project is under the Apache License 2.0. See LICENSE for details.
If you find this repository helpful, please consider citing our work:
@misc{xiang2024multimaeder,
title={MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition},
author={Peihao Xiang and Chaohao Lin and Kaida Wu and Ou Bai},
year={2024},
eprint={2404.18327},
archivePrefix={arXiv},
primaryClass={cs.CV}
}