  Zero-shot detection (ZSD) is a challenging task where we aim to recognize and
localize objects simultaneously, even when our model has not been trained with
visual samples of a few target ("unseen") classes. Recently, methods employing
generative models like GANs have shown some of the best results, where
unseen-class samples are generated based on their semantics by a GAN trained on
seen-class data, enabling vanilla object detectors to recognize unseen objects.
However, the problem of semantic confusion still remains, where the model is
sometimes unable to distinguish between semantically-similar classes. In this
work, we propose to train a generative model incorporating a triplet loss that
acknowledges the degree of dissimilarity between classes and reflects them in
the generated samples. Moreover, a cyclic-consistency loss is also enforced to
ensure that generated visual samples of a class highly correspond to their own
semantics. Extensive experiments on two benchmark ZSD datasets - MSCOCO and
PASCAL-VOC - demonstrate significant gains over the current ZSD methods,
reducing semantic confusion and improving detection for the unseen classes.
