Signformer is all you need: Towards Edge AI for Sign Language
Eta Yang

TL;DR
Signformer introduces a from-scratch, efficient transformer model for sign language translation that achieves high performance without relying on pretrained models, significantly reducing parameters and enabling edge deployment.
Contribution
This paper presents Signformer, a novel from-scratch transformer architecture for sign language translation that outperforms state-of-the-art models in efficiency and scalability without external pretrained resources.
Findings
Achieves 2nd place on leaderboard with significantly fewer parameters.
Reduces model size by 467-1807x compared to top models.
Outperforms other methods with only 0.57 million parameters.
Abstract
Sign language translation, especially in gloss-free paradigm, is confronting a dilemma of impracticality and unsustainability due to growing resource-intensive methodologies. Contemporary state-of-the-arts (SOTAs) have significantly hinged on pretrained sophiscated backbones such as Large Language Models (LLMs), embedding sources, or extensive datasets, inducing considerable parametric and computational inefficiency for sustainable use in real-world scenario. Despite their success, following this research direction undermines the overarching mission of this domain to create substantial value to bridge hard-hearing and common populations. Committing to the prevailing trend of LLM and Natural Language Processing (NLP) studies, we pursue a profound essential change in architecture to achieve ground-up improvements without external aid from pretrained models, prior knowledge transfer, or…
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Taxonomy
TopicsHand Gesture Recognition Systems · Hearing Impairment and Communication
MethodsSoftmax · Attention Is All You Need · Convolution
