# Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies

arXiv: https://arxiv.org/abs/2502.20171 · HTML: https://mlsanity.com/paper/2502.20171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?](https://mlsanity.com/paper/1907.10292/) (2019)
- [Compositional Attention: Disentangling Search and Retrieval](https://mlsanity.com/paper/2110.09419/) (2021)
- [Towards the extraction of robust sign embeddings for low resource sign language recognition](https://mlsanity.com/paper/2306.17558/) (2023)
- [ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos](https://mlsanity.com/paper/2108.10059/) (2021)

## Abstract (arXiv)

Isolated Sign Language Recognition (ISLR) is crucial for scalable sign language technology, yet language-specific approaches limit current models. To address this, we propose a one-shot learning approach that generalises across languages and evolving vocabularies. Our method involves pretraining a model to embed signs based on essential features and using a dense vector search for rapid, accurate recognition of unseen signs. We achieve state-of-the-art results, including 50.8% one-shot MRR on a large dictionary containing 10,235 unique signs from a different language than the training set. Our approach is robust across languages and support sets, offering a scalable, adaptable solution for ISLR. Co-created with the Deaf and Hard of Hearing (DHH) community, this method aligns with real-world needs, and advances scalable sign language recognition.