A zero-shot learning framework via cluster-prototype matching
作者:
Highlights:
• A novel Cluster-Prototype Matching (CPM) strategy is proposed to solve the domain shift problem of zero-shot learning.
• The distribution information of samples in the embedding space, i.e., cluster structure, is utilized to assist in zero-shot learning classification.
• The CPM strategy can be applied to most existing ZSL models in a plug-and-play style.
• Extensive experiments show the effectiveness and robustness of the proposed method.
摘要
•A novel Cluster-Prototype Matching (CPM) strategy is proposed to solve the domain shift problem of zero-shot learning.•The distribution information of samples in the embedding space, i.e., cluster structure, is utilized to assist in zero-shot learning classification.•The CPM strategy can be applied to most existing ZSL models in a plug-and-play style.•Extensive experiments show the effectiveness and robustness of the proposed method.
论文关键词:Zero-shot learning,Image classification,Cluster-prototype matching,Domain shift
论文评审过程:Received 31 May 2021, Revised 15 October 2021, Accepted 27 November 2021, Available online 29 November 2021, Version of Record 7 December 2021.
论文官网地址:https://doi.org/10.1016/j.patcog.2021.108469