A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition
Bibliographic record
Abstract
Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary modalities, such as audio, visual, and biosignals. However, most state-of-the- art audio-visual (A-V) fusion methods rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complementary nature of A-V modalities. This paper focuses on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos. We propose a joint cross-attention fusion model that can effectively exploit the complementary inter-modal relationships, allowing for an accurate prediction of valence and arousal. In particular, this model computes cross-attention weights based on the correlation between joint feature representations and individual modalities. By deploying a joint A-V feature representation into the cross-attention module, the performance of our fusion model improves significantly over the vanilla cross-attention module. Experimental results1on the AffWild2 dataset highlight the robustness of our proposed A-V fusion model. It has achieved a concordance correlation coefficient (CCC) of 0.374 (0.663) and 0.363 (0.584) for valence and arousal, respectively, on the test set (validation set). This represents a significant improvement over the baseline for the third challenge of Affective Behavior Analysis in-the-Wild 2022 (ABAW3) competition, with a CCC of 0.180 (0.310) and 0.170 (0.170).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".