Abstract
Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets, often focusing on signs with cross-linguistic similarities. This body of work examines the necessity of these likenesses on effective knowledge transfer by comparing TL performance between iconic signs of two different sign language pairs: Chinese to Arabic and Greek to Flemish. Google Mediapipe was utilised as an input feature extractor, enabling spatial information of these signs to be processed with a Multilayer Perceptron architecture and the temporal information with a Gated Recurrent Unit. Experimental results showed a 7.02% improvement for Arabic and 1.07% for Flemish when conducting iconic TL from Chinese and Greek respectively.
Abstract (translated)
大多数手语识别研究依赖于从基于视觉的数据集（如ImageNet）进行迁移学习（TL）。有些研究则扩展到使用其他可用的语言数据集，通常关注具有跨语言相似性的手势。这项工作通过比较两种不同手语对之间标志性手势的迁移学习性能来探讨这些相似性在有效知识转移中的必要性：中文至阿拉伯文和希腊文至弗拉芒语（比利时荷兰语）。谷歌Mediapipe被用作输入特征提取器，使得这些手势的空间信息可以通过多层感知机架构进行处理，时间信息则通过门控循环单元进行处理。实验结果显示，在从中文和希腊语分别进行标志性迁移学习时，阿拉伯文手语识别准确率提高了7.02%，弗拉芒语文本提高1.07%。
URL
https://arxiv.org/abs/2603.03316
