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 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