  This paper tackles the problem of zero-shot sign language recognition
(ZSSLR), where the goal is to leverage models learned over the seen sign
classes to recognize the instances of unseen sign classes. In this context,
readily available textual sign descriptions and attributes collected from sign
language dictionaries are utilized as semantic class representations for
knowledge transfer. For this novel problem setup, we introduce three benchmark
datasets with their accompanying textual and attribute descriptions to analyze
the problem in detail. Our proposed approach builds spatiotemporal models of
body and hand regions. By leveraging the descriptive text and attribute
embeddings along with these visual representations within a zero-shot learning
framework, we show that textual and attribute based class definitions can
provide effective knowledge for the recognition of previously unseen sign
classes. We additionally introduce techniques to analyze the influence of
binary attributes in correct and incorrect zero-shot predictions. We anticipate
that the introduced approaches and the accompanying datasets will provide a
basis for further exploration of zero-shot learning in sign language
recognition.
