  In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no
visual data are available at training time) can be predicted by leveraging
their class embeddings (e.g., a list of attributes describing them) together
with a complementary pool of seen classes (paired with both visual data and
class embeddings). Despite GZSL is arguably challenging, we posit that knowing
in advance the class embeddings, especially for unseen categories, is an actual
limit of the applicability of GZSL towards real-world scenarios. To relax this
assumption, we propose Open Zero-Shot Learning (OZSL) to extend GZSL towards
the open-world settings. We formalize OZSL as the problem of recognizing seen
and unseen classes (as in GZSL) while also rejecting instances from unknown
categories, for which neither visual data nor class embeddings are provided. We
formalize the OZSL problem introducing evaluation protocols, error metrics and
benchmark datasets. We also suggest to tackle the OZSL problem by proposing the
idea of performing unknown feature generation (instead of only unseen features
generation as done in GZSL). We achieve this by optimizing a generative process
to sample unknown class embeddings as complementary to the seen and the unseen.
We intend these results to be the ground to foster future research, extending
the standard closed-world zero-shot learning (GZSL) with the novel open-world
counterpart (OZSL).
