Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8073-4_25
Reference27 articles.
1. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: Nus-wide: a real-world web image database from national university of Singapore. In: Proceedings of the ACM International Conference on Image And Video Retrieval, pp. 1–9 (2009)
2. Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2075–2082 (2014)
3. Fan, L., Ng, K.W., Ju, C., Zhang, T., Chan, C.S.: Deep polarized network for supervised learning of accurate binary hashing codes. In: IJCAI, pp. 825–831 (2020)
4. Fan, W., Zhang, C., Li, H., Jia, X., Wang, G.: Three-stage semisupervised cross-modal hashing with pairwise relations exploitation. IEEE Trans. Neural Netw. Learn. Syst. Early Access, 1–14 (2023)
5. Guo, J., Guan, X.: Deep adversarial cascaded hashing for cross-modal vessel image retrieval. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 16, 2205–2220 (2023)
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