Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?

Yunus Can Bilge (Hacettepe University), Nazli Ikizler-Cinbis (Hacettepe University), Ramazan Gokberk Cinbis (METU)

We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs. To this end, we propose to utilize the readily available descriptions in sign language dictionaries as an intermediate-level semantic representation for knowledge transfer. We introduce a new benchmark dataset called ASL-Text that consists of 250 sign language classes and their accompanying textual descriptions. Compared to the ZSL datasets in other domains (such as object recognition), our dataset consists of limited number of training examples for a large number of classes, which imposes a significant challenge. We propose a framework that operates over the body and hand regions by means of 3D-CNNs, and models longer temporal relationships via bidirectional LSTMs. By leveraging the descriptive text embeddings along with these spatio-temporal representations within a zero-shot learning framework, we show that textual data can indeed be useful in uncovering sign languages. We anticipate that the introduced approach and the accompanying dataset will provide a basis for further exploration of this new zero-shot learning problem.


Paper (PDF)

title={Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?},
author={Yunus Can Bilge and Nazli Ikizler-Cinbis and Ramazan Gokberk Cinbis},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},