2022
DOI: 10.1016/j.knosys.2022.109891
|View full text |Cite
|
Sign up to set email alerts
|

Multiple instance relation graph reasoning for cross-modal hash retrieval

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 19 publications
(1 citation statement)
references
References 37 publications
0
0
0
Order By: Relevance
“…Hypergraph-based discrete hashing [34] simultaneously performs hypergraph learning and hash codes learning to enhance the semantic correlations among instances. [35] establishes multiple instance relation graphs to exploit fine-grained similarity relations between instances. Multi-view graph cross-modal hashing [36] constructs multi-view affinity and asymmetric graphs over anchor data which serve as a unified semantic hub in a semi-supervised manner.…”
Section: A Graph-based Hashing Methodsmentioning
confidence: 99%
“…Hypergraph-based discrete hashing [34] simultaneously performs hypergraph learning and hash codes learning to enhance the semantic correlations among instances. [35] establishes multiple instance relation graphs to exploit fine-grained similarity relations between instances. Multi-view graph cross-modal hashing [36] constructs multi-view affinity and asymmetric graphs over anchor data which serve as a unified semantic hub in a semi-supervised manner.…”
Section: A Graph-based Hashing Methodsmentioning
confidence: 99%