Active Learning for Multilingual Fingerspelling Corpora
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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
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
11 of 26 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
- 2017Deep Bayesian Active Learning with Image Datacited by 819
- 2020AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methodscited by 75
- 2021Including Signed Languages in Natural Language Processingcited by 68
- 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
- 2020A Transfer Learning Based Active Learning Framework for Brain Tumor Classificationcited by 8
- 2018Hand Gesture Controlled Drones: An Open Source Librarycited by 4
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.