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Towards Zero-Shot Sign Language Recognition.

Yunus Can Bilge, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 13, 2022

Summary

This summary is machine-generated.

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This study introduces zero-shot sign language recognition (ZSSLR) using textual descriptions and attributes for unseen sign classes. The findings demonstrate effective knowledge transfer for recognizing new signs, advancing sign language AI.

Area of Science:

Computer Science
Artificial Intelligence
Linguistics

Background:

Sign language recognition (SLR) traditionally requires extensive labeled data for each sign.
Zero-shot learning (ZSL) aims to recognize classes not seen during training by leveraging auxiliary information.
Existing ZSL methods often struggle with the nuanced and visual nature of sign languages.

Purpose of the Study:

To develop and evaluate a novel approach for zero-shot sign language recognition (ZSSLR).
To utilize textual descriptions and attributes from sign language dictionaries for knowledge transfer.
To establish benchmark datasets for analyzing ZSSLR performance.

Main Methods:

Development of spatiotemporal models focusing on body and hand regions.

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Integration of textual and attribute embeddings with visual representations within a ZSL framework.

Creation and utilization of three new benchmark datasets with detailed descriptions.

Main Results:

Demonstrated that textual and attribute-based class definitions effectively transfer knowledge for recognizing unseen sign classes.
Validated the proposed approach on newly introduced benchmark datasets.
Introduced methods for analyzing the impact of binary attributes on prediction accuracy.

Conclusions:

Textual and attribute information are valuable resources for enabling zero-shot sign language recognition.
The proposed ZSSLR framework and datasets provide a foundation for future research in this domain.
Further exploration of attribute-based analysis can refine zero-shot prediction capabilities.