Graph-based zero-shot learning for classifying natural and computer-generated image
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-18026-6.pdf
Reference38 articles.
1. Wang Y, Zhang H, Zhang Z et al (2020) Asymmetric graph based zero shot learning. Multimed Tools Appl 79:33689–33710. https://doi.org/10.1007/s11042-019-7689-y
2. Hanouti C, Le Borgne H (2023) Learning semantic ambiguities for zero-shot learning. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-14877-1
3. Xiao L, Hu X, Chen Y et al (2022) Multi-head self-attention based gated graph convolutional networks for aspect-based sentiment classification. Multimed Tools Appl 81:19051–19070. https://doi.org/10.1007/s11042-020-10107-0
4. Li X, Yang X, Ma Z, Xue JH (2023) Deep metric learning for few-shot image classification: a review of recent developments. Pattern Recognit 138:109381. https://doi.org/10.1016/j.patcog.2023.109381
5. Geng Y, Chen J, Zhuang X, Chen Z et al (2023) Benchmarking knowledge-driven zero-shot learning. Journal of Web Semantics 75:100757. https://doi.org/10.1016/j.websem.2022.100757
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