用轻量生成模型提升手语识别数据量，效果超越传统增强方法。
HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation
- 基于CMLPe设计轻量级手语符号生成模型，支持动态合成数据。
- 在LSFB和DiSPLaY数据集上实现新最优性能，显著提升识别准确率。
- 方法高效易用，适合资源有限的研究者快速提升手语识别效果。
手语识别（SLR）模型因训练数据不足而性能受限。本文提出一种基于CMLPe的轻量级符号生成模型，结合合成数据预训练方法，持续提升识别准确率，在使用Mamba-SL和Transformer-SL分类器时，于LSFB和DiSPLaY数据集上达到新的最佳表现。研究发现，合成数据预训练在某些情况下优于传统数据增强方法，并与之具有互补性。该方法通过计算效率高的手段，推动了手语生成与合成数据预训练的普及，可在多种数据集上实现显著性能提升。
原文摘要 · Abstract (English)
Sign Language Recognition (SLR) models face significant performance limitations due to insufficient training data availability. In this article, we address the challenge of limited data in SLR by introducing a novel and lightweight sign generation model based on CMLPe. This model, coupled with a synthetic data pretraining approach, consistently improves recognition accuracy, establishing new state-of-the-art results for the LSFB and DiSPLaY datasets using our Mamba-SL and Transformer-SL classifiers. Our findings reveal that synthetic data pretraining outperforms traditional augmentation methods in some cases and yields complementary benefits when implemented alongside them. Our approach democratizes sign generation and synthetic data pretraining for SLR by providing computationally efficient methods that achieve significant performance improvements across diverse datasets.
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