- Author
- Year
- 2017
- Title
- Zero-Shot Learning - The Good, the Bad and the Ugly
- Event
- 2017 IEEE Conference on Computer Vision and Pattern Recognition
- Book/source title
- 30th IEEE Conference on Computer Vision and Pattern Recognition
- Book/source subtitle
- CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings
- Pages (from-to)
- 3077-3086
- Publisher
- Piscataway, NJ: IEEE
- ISBN
- 9781538604588
- ISBN (electronic)
- 9781538604571
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- Abstract
-
Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Second, we compare and analyze a significant number of the state-of-the- art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss limitations of the current status of the area which can be taken as a basis for advancing it.
- URL
- go to publisher's site
- Link
- Accepted author manuscript
- Language
- English
- Persistent Identifier
- https://hdl.handle.net/11245.1/5b67fda8-1d79-4206-b607-fc16cc73560d
- Downloads
-
1703.04394.pd(Accepted author manuscript)
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