Cargando…
Deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval
Cross-modal hashing is an efficient method to embed high-dimensional heterogeneous modal feature descriptors into a consistency-preserving Hamming space with low-dimensional. Most existing cross-modal hashing methods have been able to bridge the heterogeneous modality gap, but there are still two ch...
Autores principales: | Wang, Xinyu, Zeng, Xianhua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911775/ https://www.ncbi.nlm.nih.gov/pubmed/36759692 http://dx.doi.org/10.1038/s41598-023-29320-6 |
Ejemplares similares
-
Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval
por: Cheng, Shuli, et al.
Publicado: (2020) -
Semantics-Reconstructing Hashing for Cross-Modal Retrieval
por: Zhang, Peng-Fei, et al.
Publicado: (2020) -
Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval
por: Chen, Shubai, et al.
Publicado: (2021) -
A Framework for Enabling Unpaired Multi-Modal Learning for Deep Cross-Modal Hashing Retrieval
por: Williams-Lekuona, Mikel, et al.
Publicado: (2022) -
Cross-Modal Contrastive Hashing Retrieval for Infrared Video and EEG
por: Han, Jianan, et al.
Publicado: (2022)