Abstract. We introduce and tackle the problem of zero-shot object detection
(ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves
to similar and/or fine-grained categories as in prior works on zero-shot classification. We present a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust
detectors. One of these models uses a fixed background class and the other is
based on iterative latent assignments. We also outline the challenge associated
with using a limited number of training classes and propose a solution based
on dense sampling of the semantic label space using auxiliary data with a large
number of categories. We propose novel splits of two standard detection datasets
– MSCOCO and VisualGenome, and present extensive empirical results in both
the traditional and generalized zero-shot settings to highlight the benefits of the
proposed methods. We provide useful insights into the algorithm and conclude
by posing some open questions to encourage further research