OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages (2022.acl-long)
Copied to clipboard
| Challenge: | a new study examines the performance of pretraining for sign language recognition in low-resource settings. |
| Approach: | They propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference. |
| Outcome: | The proposed model reduces training time and allows efficient inference in sign languages. |
Similar Papers
Challenges with Sign Language Datasets for Sign Language Recognition and Translation (2022.lrec-1)
Copied to clipboard
Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gómez, Mathieu De Coster, Dimitar Shterionov, Horacio Saggion
| Challenge: | Sign Languages are the primary means of communication for at least half a million people in Europe . however, the development of SL recognition and translation tools is slowed down by resource scarcity and data formats are not suitable for machine learning. |
| Approach: | They propose a framework to unify available resources and facilitate SL research for different languages. |
| Outcome: | The proposed framework is based on a set of ELAN files and returns textual and visual data ready to train SL recognition and translation models. |
SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scale (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work on sign language video processing focuses on the face, hands and body posture of the signer. |
| Approach: | They propose to learn the handshapes and rich facial expressions of sign languages in a self-supervised fashion by learning from individual frames rather than video sequences. |
| Outcome: | The proposed model is more efficient than previous work on sign language pre-training. |
Logos as a Well-Tempered Pre-train for Sign Language Recognition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing data on sign languages is limited, resulting in ambiguity in dataset labeling . similar signs can have different semantic meanings, which leads to ambiguous labeling. |
| Approach: | They propose to use a Russian sign language dataset as a universal encoder for other languages . they propose to explicitly annotate visually similar signs to improve model quality . |
| Outcome: | The proposed model outperforms current state-of-the-art models and gets competitive results for the AUTSL dataset. |
CISLR: Corpus for Indian Sign Language Recognition (2022.emnlp-main)
Copied to clipboard
Abhinav Joshi, Ashwani Bhat, Pradeep S, Priya Gole, Shashwat Gupta, Shreyansh Agarwal, Ashutosh Modi
| Challenge: | Existing work on natural language processing has shown promising improvements in text classification, translation and generation in widely used spoken languages. |
| Approach: | They propose a new Indian Sign Language corpus for word-level recognition using videos . they propose CISLR model that leverages resource rich American Sign Language to learn generalized features for improving Indian Sign language predictions. |
| Outcome: | The proposed model improves word recognition in Indian Sign Language using video . it leverages resource rich American Sign Language to learn generalized features . |
SignCLIP: Connecting Text and Sign Language by Contrastive Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | SignCLIP is an efficient method of learning useful visual representations for sign language processing from large-scale, multilingual video-text pairs without optimizing for a specific task or sign language of limited size. |
| Approach: | They propose a method for learning visual representations for sign language processing from large-scale video-text pairs without directly optimizing for a specific task or sign language. |
| Outcome: | The proposed model can learn from multilingual video-text pairs without optimizing for a specific task or sign language of limited size. |
PoseStitch-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. |
| Approach: | They propose a pose-based pre-training scheme that is inspired by a linguistic-templates-based sentence generation technique. |
| Outcome: | The proposed pre-training scheme outperforms state-of-the-art methods for pose-based gloss-free translation on two sign language datasets. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
Copied to clipboard
Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |
Including Signed Languages in Natural Language Processing (2021.acl-long)
Copied to clipboard
| Challenge: | Existing research in Sign Language Processing (SLP) rarely explores signed languages . authors urge adoption of an efficient tokenization method and the collection of real-world signed language data . |
| Approach: | They propose to include signed languages as a research area with high social and scientific impact . they review the limitations of current SLP models and identify the open challenges . |
| Outcome: | The proposed model should include signed languages as a research area with high social and scientific impact. |
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage. |
| Approach: | They present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages. |
| Outcome: | The proposed index covers 120 resources across 35 sign languages. |
Open-Domain Sign Language Translation Learned from Online Video (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on sign language translation has focused mainly on data collected in controlled environments or domains, which limits its applicability to real-world settings. |
| Approach: | They propose to use sign search as a pretext task and fusion of mouthing and handshape features to improve sign language translation in real-world settings. |
| Outcome: | The proposed techniques produce consistent and large improvements over baseline models based on prior work. |