Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation

Honghaofu Honghaofu, Liang Zhang, Biao Fu, Rui Zhao, Jinsong Su, Xiaodong Shi, Yidong Chen


Abstract
The primary objective of sign language translation (SLT) is to transform sign language videos into natural sentences.A crucial challenge in this field is developing signer-independent SLT systems which requires models to generalize effectively to signers not encountered during training.This challenge is exacerbated by the limited diversity of signers in existing SLT datasets, which often results in suboptimal generalization capabilities of current models.Achieving robustness to unseen signers is essential for signer-independent SLT.However, most existing method relies on signer identity labels, which is often impractical and costly in real-world applications.To address this issue, we propose the Signer Diversity-driven Data Augmentation (SDDA) method that can achieve good generalization without relying on signer identity labels. SDDA comprises two data augmentation schemes. The first is data augmentation based on adversarial training, which aims to utilize the gradients of the model to generate adversarial examples. The second is data augmentation based on diffusion model, which focuses on using the advanced diffusion-based text guided image editing method to modify the appearances of the signer in images. The combination of the two strategies significantly enriches the diversity of signers in the training process.Moreover, we introduce a consistency loss and a discrimination loss to enhance the learning of signer-independent features.Our experimental results demonstrate our model significantly enhances the performance of SLT in the signer-independent setting, achieving state-of-the-art results without relying on signer identity labels.
Anthology ID:
2024.findings-naacl.140
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
2182–2193
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URL:
https://aclanthology.org/2024.findings-naacl.140
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Cite (ACL):
Honghaofu Honghaofu, Liang Zhang, Biao Fu, Rui Zhao, Jinsong Su, Xiaodong Shi, and Yidong Chen. 2024. Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2182–2193, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation (Honghaofu et al., Findings 2024)
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