The Influence of Iconicity in Transfer Learning for Sign Language Recognition
arXiv:2603.03316v1 Announce Type: cross 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.
arXiv:2603.03316v1 Announce Type: cross 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.
Executive Summary
This article investigates the role of iconicity in transfer learning for sign language recognition by comparing the performance of transfer learning from Chinese to Arabic and Greek to Flemish sign languages. The authors utilize Google Mediapipe as an input feature extractor and a Multilayer Perceptron architecture with a Gated Recurrent Unit to process spatial and temporal information. The results show a 7.02% improvement for Arabic and 1.07% for Flemish when using iconic transfer learning from Chinese and Greek, respectively. This study sheds light on the importance of iconicity in sign language recognition and has implications for the development of more effective sign language recognition systems.
Key Points
- ▸ The article examines the influence of iconicity in transfer learning for sign language recognition.
- ▸ The authors compare transfer learning performance between Chinese to Arabic and Greek to Flemish sign languages.
- ▸ The study utilizes Google Mediapipe as an input feature extractor and a Multilayer Perceptron architecture with a Gated Recurrent Unit.
- ▸ The results show a significant improvement in recognition accuracy for both languages when using iconic transfer learning.
Merits
Strength of the Experimental Design
The authors employ a well-designed experimental approach, comparing transfer learning performance between two different sign language pairs, which allows for a robust evaluation of iconicity's influence on transfer learning.
Novelty of the Findings
The study's results demonstrate a significant improvement in recognition accuracy for both languages when using iconic transfer learning, which contributes to the existing body of knowledge on sign language recognition.
Demerits
Limitation of the Transfer Learning Approach
The study relies on transfer learning from pre-existing vision-based datasets, which may not be directly applicable to sign language recognition, and may not generalize to other sign languages or recognition tasks.
Lack of Exploration of Alternative Methods
The authors focus solely on iconic transfer learning, neglecting the potential benefits of other approaches, such as domain adaptation or multi-task learning, which could provide more robust recognition systems.
Expert Commentary
The article provides a valuable contribution to the field of sign language recognition and processing by shedding light on the influence of iconicity in transfer learning. The experimental design and results are robust, and the findings have significant implications for the development of more effective recognition systems. However, the study's reliance on transfer learning from pre-existing vision-based datasets and the lack of exploration of alternative methods are notable limitations. Future research could build upon this study by investigating the application of iconicity in transfer learning to other sign languages or recognition tasks, or by exploring the benefits of alternative approaches, such as domain adaptation or multi-task learning.
Recommendations
- ✓ Future research should focus on exploring the application of iconicity in transfer learning to other sign languages or recognition tasks, to further understand its influence on recognition accuracy.
- ✓ The development of more robust recognition systems should consider the integration of iconicity with other approaches, such as domain adaptation or multi-task learning, to improve recognition accuracy and efficiency.