Can a Lightweight Transformer Deliver a Robust Multimodal Sign Language Word Recognition?
Bibliographic record
Abstract
This paper addresses the challenge of dynamic word-level American Sign Language (ASL) recognition by employing a multimodal approach that relies solely on keypoints and landmarks extracted from the face and body. We use a holistic pose estimation with Mediapipe and a Transformer-based architecture to capture spatial-temporal dependencies and test the feasibility of relying on keypoints for ASL recognition. Our experiments demonstrate that even without full-frame visual input, our method achieves up to 69% top-10 accuracy on the 2,000-word subset of the WLASL dataset, highlighting the potential of contextual keypoint-based models to overcome data limitations in large-vocabulary ASL recognition tasks. Despite being implemented with a lighter-weight architecture, the proposed approach achieves performance comparable to that of heavier models that leverage full spatial-temporal context, highlighting its efficiency and practical viability for real-world applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".