The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval
Open accessAbstract
With benefits of low storage cost and fast query speed, cross-modal hashing has received considerable attention recently. However, almost all existing methods on cross-modal hashing cannot obtain powerful hash codes due to directly utilizing hand-crafted features or ignoring heterogeneous correlations across different modalities, which will greatly degrade the retrieval performance. In this paper, we propose a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities. Our architecture integrates different types of pairwise constraints to encourage the similarities of the hash codes from an intra-modal view and an inter-modal view, respectively. Moreover, additional decorrelation constraints are introduced to this architecture, thus enhancing the discriminative ability of each hash bit. Extensive experiments show that our proposed method yields state-of-the-art results on two cross-modal retrieval datasets.
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RELATIONS BETWEEN TWO SETS OF VARIATES
HOTELLING, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,263
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
NUS-WIDE: a real-world web image database from National University of Singapore
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y. (2009) · Proceedings of the ACM International Conference on Image and Video Retrieval · doi:10.1145/1646396.1646452 · Cited by 3,025
DeViSE: A Deep Visual-Semantic Embedding Model
Frome, A., Corrado, G. S., Shlens, J., Samy Bengio, A., Dean, J., Ranzato, M., & Mikolov, T. (2013) · Cited by 2,308
The MIR flickr retrieval evaluation
Huiskes, M. J., & Lew, M. S. (2008) · Proceedings of the 1st ACM international conference on Multimedia information retrieval · doi:10.1145/1460096.1460104 · Cited by 1,836
Relations Between Two Sets of Variates
Hotelling, H. (1936) · Biometrika · doi:10.2307/2333955 · Cited by 1,645
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547
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Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551
A Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241
Multi-pathway generative adversarial hashing for unsupervised cross-modal retrieval
Zhang, J., & Peng, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922128 · Cited by 103
Robust Human Movement Prediction by Completion-Generative Adversarial Networks with Huber Loss
Azari, M., Rafiei, H., & Akbarzadeh-T, M.-R. (2022) · 2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme57741.2022.10052859 · Cited by 4
Metadata
- Authors
- Yang, E., Deng, C., Liu, W., Liu, X., Tao, D., & Gao, X.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-12
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2604880013
- Cited by
- 331
- References
- 32
- FWCI
- 10.84
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Yang, E., Deng, C., Liu, W., Liu, X., Tao, D., & Gao, X. (2017). Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v31i1.10719
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