Fine-Grained Action Understanding in Instructional Sports Videos via a Hierarchical Spatiotemporal Pyramid Network
Bibliographic record
Abstract
With the digital transformation of sports education and athletic training, the automated analysis and understanding of instructional sports videos have emerged as critical areas of research.Fine-grained action understanding models play an increasingly significant role in this context, as they are designed to accurately extract and analyze detailed motion information.Traditional approaches to action recognition have primarily relied on singlescale feature extraction, which has proven inadequate for handling complex spatiotemporal information, especially in scenarios characterized by high variability and rapid motion transitions.These limitations often result in reduced accuracy and poor real-time performance.In recent years, multi-scale network models have been explored to enhance video analysis capabilities; however, challenges remain in balancing computational efficiency with precision.To address these shortcomings, a fine-grained action understanding model based on a hierarchical spatiotemporal pyramid network was proposed in this study.By constructing a multi-scale spatiotemporal pyramid prediction algorithm, this model can improve the extraction of spatiotemporal feature points of sports actions.In addition, by incorporating a temporal scale-based fine-grained action prediction algorithm, the model can capture intricate details within instructional sports videos accurately.By optimizing dynamic spatiotemporal characteristics and temporal dependencies, this study achieves improved accuracy and real-time performance in the prediction of fine-grained sports actions, offering a novel theoretical and technical foundation for the development of intelligent sports instruction systems.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".