Incongruity-Aware Cross-Modal Attention for Audio-Visual Fusion in Dimensional Emotion Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it