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 threefold. First, given the fact that there is no agreed upon zeroshot 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-theart 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.
A more comprehensive review about the zero-shot learning can be found in REF .
Zero-Shot Learning — The Good, the Bad and the Ugly
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science