The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Asymmetric Deep Supervised Hashing
Open accessAbstract
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
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
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Supervised hashing with kernels
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Cited by
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
Deep balanced discrete hashing for image retrieval
Zheng, X., Zhang, Y., & Lu, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.037 · Cited by 83
Deep Perceptual Hash Based on Hash Center for Image Copyright Protection
Sun, X., & Zhou, J. (2022) · IEEE Access · doi:10.1109/access.2022.3221980 · Cited by 20
“The representative ones are DSH [29], DPSH [30], HashNet [31], ADSH [32] etc.”
BackgroundMachine Unlearning for Image Retrieval: A Generative Scrubbing Approach
Zhang, P.-F., Bai, G., Huang, Z., & Xu, X.-S. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3548378 · Cited by 18
Metadata
- Authors
- Jiang, Q.-Y., & Li, W.-J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-29
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2962786441
- Cited by
- 266
- References
- 35
- FWCI
- 9.31
- Type
- conference paper
- Language
- en
- Open access
- diamond
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