Transductive zero-shot action recognition via visually connected graph convolutional networks

• Main Authors: XU, Yangyang, HAN, Chu, QIN, Jing, XU, Xuemiao, HAN, Guoqiang, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Visualization; Feature extraction; Semantics; Correlation; Computational modeling; Learning systems; Explosives; Action recognition; graph convolutional network (GCN); zero-shot learning (ZSL); Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7883
• Institution: Singapore Management University

With the explosive growth of action categories, zero-shot action recognition aims to extend a well-trained model to novel/unseen classes. To bridge the large knowledge gap between seen and unseen classes, in this brief, we visually associate unseen actions with seen categories in a visually connecte...