Self-Supervised Video Interaction Classification using Image Representation of Skeleton Data
Bibliographic record
Abstract
Recognizing interactions from sports games broadcast videos is an application of Interaction Recognition from Videos (IRV), that offers many challenges due to complex interactions that are often recorded from a suboptimal view point. Annotating large scale sports specific datasets is expensive and time-consuming. Therefore, in this study, we propose to demonstrate the effectiveness of applying Self-Supervised Learning (SSL) methods for building useful representations from human skeleton pose data (pose for short) without requiring costly annotations for a large scale dataset. Given the numerous well established image-based SSL methods, we demonstrate how to adapt them for sequences of pose through data transformation and a series of pose-based augmentations. We specifically adapt the Relational Reasoning SSL (Relational-SSL for short) [27] and achieve 68.18 ± 0% and 76.62 ± 2.7% in linear evaluation and finetuning protocols, respectively, for the downstream task of IRV from sports broadcast videos. Lastly, we run ablation studies on different components of the method, including the effect of using estimated pose (versus ground truth) on the performance of the downstream task. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
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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 | 0.001 |
| 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".