Papers › MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
Peihao Xiang, Chaohao Lin, Kaida Wu, Ou Bai
This paper presents a novel approach to processing multimodal data for dynamic emotion recognition, named as the Multimodal Masked Autoencoder for Dynamic Emotion Recognition (MultiMAE-DER). The MultiMAE-DER leverages the closely correlated representation information within spatiotemporal sequences across visual and audio modalities. By utilizing a pre-trained masked autoencoder model, the MultiMAEDER is accomplished through simple, straightforward finetuning. The performance of the MultiMAE-DER is enhanced by optimizing six fusion strategies for multimodal input sequences. These strategies address dynamic feature correlations within cross-domain data across spatial, temporal, and spatiotemporal sequences. In comparison to state-of-the-art multimodal supervised learning models for dynamic emotion recognition, MultiMAE-DER enhances the weighted average recall (WAR) by 4.41% on the RAVDESS dataset and by 2.06% on the CREMAD. Furthermore, when compared with the state-of-the-art model of multimodal self-supervised learning, MultiMAE-DER achieves a 1.86% higher WAR on the IEMOCAP dataset.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition | RAVDESS | MultiMAE-DER | WAR | 83.61% | #5 of 5 | Archive leaderboard | report |
| Multimodal Emotion Recognition | IEMOCAP-4 | MultiMAE-DER | Weighted Recall | 63.73 | #11 of 11 | Archive leaderboard | report |
| Video Emotion Recognition | CREMA-D | MultiMAE-DER | WAR | 79.36% | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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