,
pages 73-90
Knowledge-Based VQA
4
AI Lab, JD.COM, Beijing, China
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Publication type: Book Chapter
Publication date: 2022-05-13
SJR: —
CiteScore: 2.7
Impact factor: —
ISSN: 21916586, 21916594
Abstract
Tasks such as VQA often require common sense and factual information in addition to the information learned from a task-specific dataset. Therefore, a knowledge-based VQA task is established. In this chapter, we first introduce the main datasets proposed for knowledge-based VQA and knowledge bases such as DBpediaDBpedia and ConceptNetConceptNet. Subsequently, we classify methods from three aspects: knowledge embedding, question-to-query translation and querying knowledge base methods.
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TY - GENERIC
DO - 10.1007/978-981-19-0964-1_5
UR - https://doi.org/10.1007/978-981-19-0964-1_5
TI - Knowledge-Based VQA
T2 - Advances in Computer Vision and Pattern Recognition
AU - Wu, Qiang
AU - Wang, Peng
AU - Wang, Xin
AU - He, Xiaodong
AU - Zhu, Wenwu
PY - 2022
DA - 2022/05/13
PB - Springer Nature
SP - 73-90
SN - 2191-6586
SN - 2191-6594
ER -
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@incollection{2022_Wu,
author = {Qiang Wu and Peng Wang and Xin Wang and Xiaodong He and Wenwu Zhu},
title = {Knowledge-Based VQA},
publisher = {Springer Nature},
year = {2022},
pages = {73--90},
month = {may}
}