Multimodal cross-context recognition of negative interactions
Bibliographic record
Abstract
Negative emotions and stress can impact human-human interactions and eventually lead to aggression. From the perspective of surveillance systems, it is of high importance to recognize as soon as an interaction escalates and human intervention is needed. One of the limitations of deploying a system in real life is that in practice it can only be trained on a limited number of situations. In this paper we examined the generalization capabilities of a trained system given context change. For this purpose we developed scenarios and made audio-visual recordings in four different contexts in which negative interactions might occur. To obtain a quantification of cross-context performance we kept the test context fixed and performed training on itself (cross-validation) and on all the other contexts. To explore whether multiple examples in the training set are beneficial, we also trained the classifier on a merged corpus of the three contexts that were not used for testing. These experiments were done with audio features, video features and audio-visual feature level fusion to investigate which modality generalizes best. We found that context change generates a decrease in performance that is varying with within-contexts similarities. Merging multiple contexts for training in most cases results in performance just below the best predictive single context. Audio is the most robust modality and in most cases the performance of audio-visual fusion is very close to the one of audio.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".