Paper Info
Title
Knowledge is Power: Hierarchical-Knowledge Embedded Meta-Learning for Visual Reasoning in Artistic Domains
Abstract
ABSTRACTThis paper deals with the challenging problem of building visual reasoning models for answering questions related to artworks in artistic domains. The nature of abstract styles and cultural contexts within an artistic image makes the corresponding learning tasks extremely difficult. We propose a novel framework termed as Hierarchical-Knowledge Embedded Meta-Learning to address the critical issues of visual reasoning in artistic domains. In particular, we firstly present a deep relational model to capture and memorize the relations among different samples. Then, we provide the hierarchical-knowledge embedding that mines the implicit relationship between question-answer pairs for knowledge representation as the guidance of our meta-learner. This is a case of "knowledge is power" in the sense that the hierarchical knowledge representation is incorporated into our meta-learning based model. The final classification is derived from our model by learning to compare the features of samples. Experimental results show that our approach achieves significantly higher performance compared with other state-of-the-arts.
Year
DOI
Venue
2021
10.1145/3447548.3467285
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Knowledge Embedding, Meta-Learning, Visual Reasoning, Art
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wenbo Zheng1155.59
Lan Yan2127.91
Chao Gou313319.52
Fei-Yue Wang45273480.21