Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment
A groundbreaking method for recognizing sign language has been developed by researchers, utilizing transfer learning and domain adaptation techniques. This approach, known as TA3N, incorporates a Temporal Relational Network (TRN) module to synchronize multi-scale temporal relationships. Published on arXiv (2608.16804), the study tackles the critical shortage of recognition resources for more than 100 sign languages. Results indicate that domain adaptation surpasses traditional neural network-based transfer learning, particularly enhancing the recognition of American Sign Language (ASL). The research emphasizes the importance of aligning short-term temporal features between source and target domains. Experiments were carried out using both RGB and Optical Flow modes, with RGB showing better performance in most instances. The ultimate goal is to enhance accessibility for those with hearing impairments.
Key facts
- The study uses transfer learning and domain adaptation method TA3N for sign language recognition.
- TA3N utilizes the Temporal Relational Network (TRN) module for aligning multi-scale temporal relations.
- Domain adaptation outperforms neural network-based transfer learning, especially for American Sign Language (ASL).
- Aligning shorter-term temporal features between source and target domains is effective.
- Experiments were conducted using RGB and Optical Flow modes.
- RGB outperforms Optical Flow in the majority of cases.
- The research addresses the lack of recognition resources for over 100 distinct sign languages.
- The work aims to improve accessibility for individuals with hearing impairments.
Entities
Institutions
- arXiv