图标性在手语识别中迁移学习中的影响
The Influence of Iconicity in Transfer Learning for Sign Language Recognition
AI总结：
本研究探讨图标性在手语识别迁移学习中的影响，通过比较不同手语对的性能差异，发现图标性手语的迁移学习能显著提升识别效果。
摘要：
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）。一些研究将其扩展到其他可用的语言数据集，通常关注具有跨语言相似性的手语。本研究通过比较两种不同手语对（中文到阿拉伯语和希腊语到弗拉芒语）的图标性手语之间的TL性能，探讨这些相似性在有效知识迁移中的必要性。Google Mediapipe被用作输入特征提取器，使这些手语的空间信息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。实验结果表明，当从中文和希腊语进行图标性TL时，阿拉伯语的性能提升了7.02%，弗拉芒语的性能提升了1.07%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
