  Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localization' of an unseen category. To address this limitation, we introduce
a new \emph{`Zero-Shot Detection'} (ZSD) problem setting, which aims at
simultaneously recognizing and locating object instances belonging to novel
categories without any training examples. We also propose a new experimental
protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to
practical issues, e.g., the rarity of unseen objects. To the best of our
knowledge, this is the first end-to-end deep network for ZSD that jointly
models the interplay between visual and semantic domain information. To
overcome the noise in the automatically derived semantic descriptions, we
utilize the concept of meta-classes to design an original loss function that
achieves synergy between max-margin class separation and semantic space
clustering. Furthermore, we present a baseline approach extended from
recognition to detection setting. Our extensive experiments show significant
performance boost over the baseline on the imperative yet difficult ZSD
problem.
