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

	A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition
	Jinting Wu1,2 ; Yujia Zhang1 ; Xiaoguang Zhao1
	2021-01
会议名称	2020 25th International Conference on Pattern Recognition (ICPR)
会议日期	2021-1
会议地点	Online
摘要	Hand gesture recognition plays a signifificant 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 fifirst 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 specififically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures.
收录类别	EI
七大方向——子方向分类	图像视频处理与分析
国重实验室规划方向分类	视觉信息处理
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49703
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jinting Wu,Yujia Zhang,Xiaoguang Zhao. A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition[C],2021.

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