Deep Uncertainty Interpretation in Dyadic Human Activity Prediction
Bibliographic record
Abstract
We propose a deep learning framework to analyse the uncertainty associated with dyadic human activities at a small temporal granularity. Such time-slice analysis is able to infer human behaviours from short-term observations. Instead of classifying time-slices into k classes of activities, we report to what degree of certainty each activity is occurring from definitely not occurring to definitely occurring. To this end, we extract CNN-based unary probabilities and pairwise relations between body joints. The unary term gives cues on the local appearance while the pairwise term captures the contextual relations between the parts. We extract the features from each frame in a timeslice and examine different temporal aggregation schemes to generate a descriptor for the whole time-slice. Evaluations on the TAP dataset which is well-suited for time-slice activity analysis demonstrate the effectiveness of our approach for the task of uncertainty analysis in activity prediction.
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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".