Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03316 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911483460321280 |
|---|---|
| author | Artiaga, Keren Lynch, Conor Afli, Haithem Hasanuzzaman, Mohammed |
| author_facet | Artiaga, Keren Lynch, Conor Afli, Haithem Hasanuzzaman, Mohammed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Influence of Iconicity in Transfer Learning for Sign Language Recognition Artiaga, Keren Lynch, Conor Afli, Haithem Hasanuzzaman, Mohammed Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition I.2.10; I.5.4 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. |
| title | The Influence of Iconicity in Transfer Learning for Sign Language Recognition |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition I.2.10; I.5.4 |
| url | https://arxiv.org/abs/2603.03316 |