Look who is not talking: Assessing engagement levels in panel conversations
Bibliographic record
Abstract
Nonverbal cues constitute a significant part of human communication. Traditionally the object of psychology, nonverbal communication studies now permeate fields such as social signal processing and human computer interaction. The ubiquity of digital recordings of human social interactions and of free sharing platforms offers many opportunities for the automated analysis of group interaction dynamics; yet, most research relies on multimodal cues and strict setups, which are incompatible with this vast pool of video data. In this paper, we focus on the automatic identification of non-talking participants in videos of panel conversations acquired in uncontrolled environments, based solely on visual nonverbal cues. Our approach characterizes human body motion with a novel feature descriptor based on a non-linear model of pixel change history; motor behavioral patterns derived from this descriptor are then utilized via supervised machine learning to identify non-talking participants in each frame and provide an assessment of the participants' engagement levels. Performance evaluation on a challenging dataset demonstrated the effectiveness of our approach to detect non-speakers, with an overall F-score of 86.2%, as well as its robustness to varied settings. To the best of our knowledge, this is the first attempt at identifying non-talking participants for engagement level assessments from a computer vision viewpoint, which has several relevant applications, such as content-based video retrieval and video summarization.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".