How visual chirality affects the performance of image hashing
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-08141-0.pdf
Reference35 articles.
1. Wang Y, Song J, Zhou K, Liu Y (2021) Unsupervised deep hashing with node representation for image retrieval. Pattern Recognit 112:107785. https://doi.org/10.1016/j.patcog.2020.107785
2. Zhou K, Liu Y, Song J, Yan L, Zou F, Shen F (2015) Deep self-taught hashing for image retrieval. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM, pp 1215–1218. ACM, Brisbane. https://doi.org/10.1145/2733373.2806320
3. Liu Y, Wang Y, Song J, Guo C, Zhou K, Xiao Z (2020) Deep self-taught graph embedding hashing with pseudo labels for image retrieval. In: IEEE International Conference on Multimedia and Expo, ICME, pp 1–6. IEEE, London. https://doi.org/10.1109/ICME46284.2020.9102819
4. Liu Y, Song J, Zhou K, Yan L, Liu L, Zou F, Shao L (2019) Deep self-taught hashing for image retrieval. IEEE Trans Cybern 49(6):2229–2241. https://doi.org/10.1109/ICME46284.2020.9102819
5. Hoorick BV, Vondrick C (2021) Dissecting image crops. In: 2021 IEEE/CVF International Conference on Computer Vision, ICCV, pp 9721–9730. IEEE, Montreal. https://doi.org/10.1109/ICCV48922.2021.00960
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