Title
Ontology-guided Semantic Composition for Zero-Shot Learning
Abstract
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering.
Year
DOI
Venue
2020
10.24963/kr.2020/87
KR
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
J Chen113930.64
Freddy Lécué263450.52
Yuxia Geng322.12
Jeff Z. Pan42218158.01
Huanhuan Chen5731101.79