# Active Learning for Multilingual Fingerspelling Corpora

arXiv: https://arxiv.org/abs/2309.12443 · HTML: https://mlsanity.com/paper/2309.12443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Active Learning Helps Pretrained Models Learn the Intended Task](https://mlsanity.com/paper/2204.08491/) (2022)
- [A Transfer Learning Based Active Learning Framework for Brain Tumor Classification](https://mlsanity.com/paper/2011.09265/) (2020)
- [Hand Gesture Controlled Drones: An Open Source Library](https://mlsanity.com/paper/1803.10344/) (2018)

## Built on by (most cited first, 2 of 2)

- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [RAIDAL: Redundancy-Aware Information Density Active Learning for CTC-Based Continuous Sign Language Recognition](https://mlsanity.com/paper/2609.06843/) (2026)

## Abstract (arXiv)

We apply active learning to help with data scarcity problems in sign languages. In particular, we perform a novel analysis of the effect of pre-training. Since many sign languages are linguistic descendants of French sign language, they share hand configurations, which pre-training can hopefully exploit. We test this hypothesis on American, Chinese, German, and Irish fingerspelling corpora. We do observe a benefit from pre-training, but this may be due to visual rather than linguistic similarities