Work

Benchmarking Data Augmentation for Contrastive Learning in Static Sign Language Recognition

Year: 2025

Type: conference-paper

Abstract: Sign language (SL) is a communication method used by deaf people.Static sign language recognition (SLR) is a challenging task aimed at identifying signs in images, for which acquisition of annotated data is time-consuming.To leverage unannotated data, practitioners have turned to unsupervised methods.Contrastive representation learning proved to be effective in capturing important features from unannotated data.It is known that the performance of the contrastive model depends on the data augmentation technique used during training.For various applications, a set of effective data augmentation has been identified, but it is not yet the case for SL.This paper identifies the most effective augmentation for static SLR.The results show a difference in accuracy of up to 30% between appearance-based augmentations combined with translations and augmentations based on rotations, erasing, or vertical flips.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-142

Authors: Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Bélise Kenmogne Edith, Benoit Frénay

Institution: University of Namur

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture

OpenAlex ID: W4409480391

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