EPT-MoE: Toward Efficient Parallel Transformers with Mixture-of-Experts for 3D Hand Gesture Recognition
Bibliographic record
Abstract
The Mixture-of-Experts (MoE) is a widely known deep neural architecture where an ensemble of specialized sub-models (a group of experts) optimizes the overall performance with a constant computational cost.Especially with the rise of Mixture-of-Experts with Mixtral-8x7B Transformers, MoE architectures have gained popularity in Large Language Modeling (LLM) and Computer Vision.In this paper, we propose the Efficient Parallel Transformers of Mixture-of-Experts (EPT-MoE) coupled with Spatial Feed Forward Neural Networks (SFFN) to enhance the ability of parallel Transformer models with Mixture-of-Experts layers for graph learning of 3D skeleton-data hand gesture recognition.Nowadays, 3D hand gesture recognition is an attractive field of research in human-computer interaction, VR/AR and pattern recognition.For this purpose, our proposed EPT-MoE model decouples the spatial and temporal graph learning of 3D hand gestures by integrating mixture-of-experts layers into parallel Transformer models.The main idea is to combine the powerful layers of mixture-of-experts that process the initial spatial features of intra-frame interactions to extract powerful features from different hand joints, and then, to recognize 3D hand gestures within the parallel Transformer encoders with layers of Mixture-of-Experts.Finally, we conduct extensive experiments on benchmarks of the SHREC'17 Track dataset in order to evaluate the performance of EPT-MoE model variations.EPT-MoE greatly improves the overall performance, the training stability and reduces the computational cost.The experimental results show the efficiency of several variants of the proposed model (EPT-MoE), which achieves or outperforms the state-of-the-art.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".