Improving loss function for deep convolutional neural network applied in automatic image annotation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-02873-3.pdf
Reference43 articles.
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3. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011). https://doi.org/10.1007/s10994-011-5256-5
4. Zhu, G., Yan, S., Ma, Y.: Image tag refinement towards low-rank, content-tag prior and error sparsity. In: MM’10—Proceedings of the ACM Multimedia 2010 International Conference. 2010, pp. 461–470. https://doi.org/10.1145/1873951.1874028.
5. Jin, J., Nakayama, H.: Annotation order matters: recurrent image annotator for arbitrary length image tagging. In: Proceedings—International Conference on Pattern Recognition. pp. 2452–2457, (2016). https://doi.org/10.1109/ICPR.2016.7900004.
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1. Enhancing high-vocabulary image annotation with a novel attention-based pooling;2023-08-23
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