Statistical vs. visual data generation in hand gesture recognition
Bibliographic record
Abstract
A dataset with diverse training data is essence of the hand gesture recognition research. Most of the benchmarked datasets are limited in the number of signers and/or the number of each gesture try, which often result in over-fitting and poor generalization. Overcoming this challenge is often achieved by collecting a large number of exemplars for each hand gesture. This process is either expensive or impractical. Recently, synthetic data generation methods have been presented as a more reliable way to extend and enrich datasets. This paper proposes a comparative study of statistical and visual synthetic data generation. The visual synthetic data generation is executed by building synthetic 3D animated models using human figure design software. The experiments illustrate how the recognition accuracy will be changed when both methods used in enlarging the training data. The results show that in both cases recognition accuracy is enhanced, and in most cases, the visual synthetic data enlargement provides better improvement in the quality and diversity of the training data.
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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 | 0.001 | 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 | 0.001 |
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 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".