Incongruity-Aware Cross-Modal Attention for Audio-Visual Fusion in Dimensional Emotion Recognition
Bibliographic record
Abstract
Multimodal emotion recognition has immense potential for the comprehensive assessment of human emotions, utilizing multiple modalities that often exhibit complementary relationships. In video-based emotion recognition, audio and visual modalities have emerged as prominent contact-free channels, widely explored in existing literature. Current approaches typically employ cross-modal attention mechanisms between audio and visual modalities, assuming a constant state of complementarity. However, this assumption may not always hold true, as non-complementary relationships can also manifest, undermining the efficacy of cross-modal feature integration and thereby diminishing the quality of audio-visual feature representations. To tackle this problem, we introduce a novel Incongruity-Aware Cross-Attention (IACA) model, capable of harnessing the benefits of robust complementary relationships while efficiently managing non-complementary scenarios. Specifically, our approach incorporates a two-stage gating mechanism designed to adaptively select semantic features, thereby effectively capturing the inter-modal associations. Additionally, the proposed model demonstrates an ability to mitigate the adverse effects of severely corrupted or missing modalities. We rigorously evaluate the performance of the proposed model through extensive experiments conducted on the challenging RECOLA and Aff-Wild2 datasets. The results underscore the efficacy of our approach, as it outperforms state-of-the-art methods by adeptly capturing inter-modal relationships and minimizing the influence of missing or heavily corrupted modalities. Furthermore, we show that the proposed model is compatible with various cross-modal attention variants, consistently improving performance on both datasets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".