Semantic preserving asymmetric discrete hashing for cross-modal retrieval-Reference-Cited by-同舟云学术

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Semantic preserving asymmetric discrete hashing for cross-modal retrieval

Published:2022-11-15 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Yang Fan^ORCID,Zhang Qiao-xi,Ding Xiao-jian,Ma Fu-min,Cao Jie,Tong De-yu

Funder

National Natural Science Foundation of China

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Natural Science Foundation of Jiangsu Province

International Science and Technology Cooperation Project of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-04282-w.pdf

Reference47 articles.

1. Chen ZD, Wang Y, Li HQ et al (2021) A two-step cross-modal hashing by exploiting label correlations and preserving similarity in both steps. In: Proceedings of the 27th ACM international conference on multimedia. https://doi.org/10.1145/3343031.3350862

2. Yang F, Liu YF, Ding XJ et al (2022) Asymmetric cross-modal hashing with high-level semantic similarity. Pattern Recogn 130:108823. https://doi.org/10.1016/j.patcog.2022.108823

3. Hu P, Zhu HY, Lin J et al (2022) Unsupervised contrastive cross-modal hashing. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2022.3177356https://doi.org/10.1109/TPAMI.2022.3177356

4. Fang YX, Zhang HX, Ren YW (2019) Unsupervised cross-modal retrieval via multi-modal graph regularized smooth matrix factorization hashing. Knowl-Based Syst 171:69–80. https://doi.org/10.1016/j.knosys.2019.02.004

5. Cheng D, Yang E, Liu T, et al. (2019) Unsupervised semantic-preserving adversarial hashing for image search. IEEE Trans Image Process 28:4032–4044. https://doi.org/10.1109/TIP.2019.2903661

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