对比了循环与注意力模型在手语识别中的表现，发现注意力模型更准但更耗资源。
A Comparative Analysis of Recurrent and Attention Architectures for Isolated Sign Language Recognition
- 用ConvLSTM和Transformer分别处理手语视频数据
- Transformer在两个数据集上准确率均更高，最高达88.3%
- 适合对精度要求高、资源充足的场景
本研究系统比较了用于孤立手语识别的循环与注意力神经架构。我们在阿塞拜疆手语数据集（AzSLD）和单词级美国手语数据集（WLASL）上实现并评估了两种代表性模型——ConvLSTM与Vanilla Transformer。结果表明，注意力型Vanilla Transformer在两个数据集上的Top-1与Top-5准确率均持续优于循环型ConvLSTM，分别达到AzSLD上76.8%的Top-1准确率和WLASL上88.3%的准确率。ConvLSTM虽计算效率更高，但在小规模数据集上识别准确率偏低。研究揭示了两类架构的互补优势：Transformer在整体准确率和说话人无关性方面更优，而ConvLSTM在计算效率与时间建模上占优。该分析为不同应用场景下的架构选择提供了依据。
原文摘要 · Abstract (English)
This study presents a systematic comparative analysis of recurrent and attention-based neural architectures for isolated sign language recognition. We implement and evaluate two representative models-ConvLSTM and Vanilla Transformer-on the Azerbaijani Sign Language Dataset (AzSLD) and the Word-Level American Sign Language (WLASL) dataset. Our results demonstrate that the attention-based Vanilla Transformer consistently outperforms the recurrent ConvLSTM in both Top-1 and Top-5 accuracy across datasets, achieving up to 76.8% Top-1 accuracy on AzSLD and 88.3% on WLASL. The ConvLSTM, while more computationally efficient, lags in recognition accuracy, particularly on smaller datasets. These findings highlight the complementary strengths of each paradigm: the Transformer excels in overall accuracy and signer independence, whereas the ConvLSTM offers advantages in computational efficiency and temporal modeling. The study provides a nuanced analysis of these trade-offs, offering guidance for architecture selection in sign language recognition systems depending on application requirements and resource constraints.
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