  Hand gesture recognition plays a significant role in human-computer
interaction for understanding various human gestures and their intent. However,
most prior works can only recognize gestures of limited labeled classes and
fail to adapt to new categories. The task of Generalized Zero-Shot Learning
(GZSL) for hand gesture recognition aims to address the above issue by
leveraging semantic representations and detecting both seen and unseen class
samples. In this paper, we propose an end-to-end prototype-based GZSL framework
for hand gesture recognition which consists of two branches. The first branch
is a prototype-based detector that learns gesture representations and
determines whether an input sample belongs to a seen or unseen category. The
second branch is a zero-shot label predictor which takes the features of unseen
classes as input and outputs predictions through a learned mapping mechanism
between the feature and the semantic space. We further establish a hand gesture
dataset that specifically targets this GZSL task, and comprehensive experiments
on this dataset demonstrate the effectiveness of our proposed approach on
recognizing both seen and unseen gestures.
