# The Influence of Iconicity in Transfer Learning for Sign Language Recognition

arXiv: https://arxiv.org/abs/2603.03316 · HTML: https://mlsanity.com/paper/2603.03316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi](https://mlsanity.com/paper/2003.04986/) (2020)
- [Isolated Sign Language Recognition based on Tree Structure Skeleton Images](https://mlsanity.com/paper/2304.05403/) (2023)
- [Adapting Multilingual Neural Machine Translation to Unseen Languages](https://mlsanity.com/paper/1910.13998/) (2019)
- [Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/2010.07827/) (2020)
- [Optimization of Transfer Learning for Sign Language Recognition Targeting Mobile Platform](https://mlsanity.com/paper/1805.06618/) (2018)
- [Sign Language to Text Conversion in Real Time using Transfer Learning](https://mlsanity.com/paper/2211.14446/) (2022)

## Abstract (arXiv)

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.