Swin PoseFormer: A Human Activity Recognition Model Using Skeleton Heatmaps
Bibliographic record
Abstract
Human Activity Recognition has undergone significant advancements lately. Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and environmental robustness. Many skeleton-based action recognition methods adopt graph convolutional networks to extract features from human skeleton data. Recently, Transformer models have shown great success in modeling long-range interactions. Existing research has shown that self-attention mechanism is promising in solving video processing tasks. In this paper, we propose a novel network, Swin PoseFormer, which relies on Swin Transformer backbone to process a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons. We test our Swin Poseformer model on the FineGym dataset and demonstrate that our model outperforms the state of the art model with top-1 and top-5 accuracy of 84.32% and 98.45% respectively.
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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) | 0.000 | 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".