Class Representative Learning for Zero-shot Learning Using Purely Visual Data

Author:

Chandrashekar MayankaORCID,Lee Yugyung

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Reference87 articles.

1. Wang W, Zheng VW, Yu H, Miao C. A survey of zero-shot learning: Settings, methods, and applications. ACM Trans Intell Syst Technol. 2019;10(2):13.

2. Lake B, Salakhutdinov R, Gross J, Tenenbaum J. One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society; 2011. p. 33.

3. Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al. Matching networks for one shot learning. In: Advances in neural information processing systems; 2016. pp. 3630–8.

4. Lampert CH, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell. 2013;36(3):453–65.

5. Akata Z, Perronnin F, Harchaoui Z, Schmid C. Label-embedding for image classification. IEEE Trans Pattern Anal Mach Intell. 2015;38(7):1425–38.

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