[2603.03316] The Influence of Iconicity in Transfer Learning for Sign Language Recognition

Computer Science > Computation and Language arXiv:2603.03316 (cs) [Submitted on 9 Feb 2026] Title:The Influence of Iconicity in Transfer Learning for Sign Language Recognition Authors:Keren Artiaga, Conor Lynch, Haithem Afli, Mohammed Hasanuzzaman View a PDF of the paper titled The Influence of Iconicity in Transfer Learning for Sign Language Recognition, by Keren Artiaga and 3 other authors View PDF HTML (experimental) 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. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.2.10; I.5.4 Cite as: arXiv:2603.03316 [cs.CL]   (or arXiv:2603.03316v1 [cs.CL] for this version)  ...