EPT-MoE: Toward Efficient Parallel Transformers with Mixture-of-Experts for 3D Hand Gesture Recognition
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it