Papers › Self-attention fusion for audiovisual emotion recognition with incomplete data
Self-attention fusion for audiovisual emotion recognition with incomplete data
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms. While most of the previous works consider the ideal scenario of presence of both modalities at all times during inference, we evaluate the robustness of the model in the unconstrained settings where one modality is absent or noisy, and propose a method to mitigate these limitations in a form of modality dropout. Most importantly, we find that following this approach not only improves performance drastically under the absence/noisy representations of one modality, but also improves the performance in a standard ideal setting, outperforming the competing methods.
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Code
Syntology Ran 7 of 8 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 5 ran with no contract checked.
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Code Syntology ran Syntology
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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 | Intermediate-Attention-Fusion | Accuracy | 81.58% | #2 of 5 | Archive leaderboard | report |
| Facial Emotion Recognition | RAVDESS | Intermediate-Transformer-Fusion, visual branch only | Accuracy | 74.92% | #2 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.
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