Video Interaction Recognition using an Attention Augmented Relational Network and Skeleton Data
Bibliographic record
Abstract
Recognizing interactions in multi-person videos, known as Video Interaction Recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".