轻量级模型EfficientSign实现高精度印度手语识别，适合手机部署。
EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition
- 基于EfficientNet-B0改进，加入通道与空间注意力模块。
- 99.94%准确率，参数量仅ResNet18的38%。
- 无需复杂特征工程，适合移动端实时应用。
如何构建能在手机上运行的手语识别系统？为此我们提出了EfficientSign，一种轻量级模型，以EfficientNet-B0为基础，引入通道注意力（Squeeze-and-Excitation）和空间注意力模块聚焦手部动作。在包含12,637张图像、26个类别的印度手语字母数据集上，采用5折交叉验证，EfficientSign达到99.94%（±0.05%）准确率，与ResNet18的99.97%相当，但参数量减少62%（420万 vs 1120万）。进一步实验表明，从EfficientNet-B0池化层提取的1280维深度特征输入经典分类器：SVM达99.63%，逻辑回归99.03%，KNN为96.33%。所有结果均显著优于2015年基于SURF的方法（92%）。结果表明，注意力增强模型可实现高效且可部署的ISL识别，无需庞大模型或人工调参特征流水线。
原文摘要 · Abstract (English)
How do you build a sign language recognizer that works on a phone? That question drove this work. We built EfficientSign, a lightweight model which takes EfficientNet-B0 and focuses on two attention modules (Squeeze-and-Excitation for channel focus, and a spatial attention layer that focuses on the hand gestures). We tested it against five other approaches on 12,637 images of Indian Sign Language alphabets, all 26 classes, using 5-fold cross-validation. EfficientSign achieves the accuracy of 99.94% (+/-0.05%), which matches the performance of ResNet18's 99.97% accuracy, but with 62% fewer parameters (4.2M vs 11.2M). We also experimented with feeding deep features (1,280-dimensional vectors pulled from EfficientNet-B0's pooling layer) into classical classifiers. SVM achieved the accuracy of 99.63%, Logistic Regression achieved the accuracy of 99.03% and KNN achieved accuracy of 96.33%. All of these blow past the 92% that SURF-based methods managed on a similar dataset back in 2015. Our results show that attention-enhanced learning model provides an efficient and deployable solution for ISL recognition without requiring a massive model or hand-tuned feature pipelines anymore.
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