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Hand gesture recognition plays a significant role in human-computerinteraction for understanding various human gestures and their intent. However,most prior works can only recognize gestures of limited labeled classes andfail to adapt to new categories. The task of Generalized Zero-Shot Learning(GZSL) for hand gesture recognition aims to address the above issue byleveraging semantic representations and detecting both seen and unseen classsamples. In this paper, we propose an end-to-end prototype-based GZSL frameworkfor hand gesture recognition which consists of two branches. The first branchis a prototype-based detector that learns gesture representations anddetermines whether an input sample belongs to a seen or unseen category. Thesecond branch is a zero-shot label predictor which takes the features of unseenclasses as input and outputs predictions through a learned mapping mechanismbetween the feature and the semantic space. We further establish a hand gesturedataset that specifically targets this GZSL task, and comprehensive experimentson this dataset demonstrate the effectiveness of our proposed approach onrecognizing both seen and unseen gestures.

A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition