  The Zero-Shot Learning (ZSL) task attempts to learn concepts without any
labeled data. Unlike traditional classification/detection tasks, the evaluation
environment is provided unseen classes never encountered during training. As
such, it remains both challenging, and promising on a variety of fronts,
including unsupervised concept learning, domain adaptation, and dataset drift
detection. Recently, there have been a variety of approaches towards solving
ZSL, including improved metric learning methods, transfer learning,
combinations of semantic and image domains using, e.g. word vectors, and
generative models to model the latent space of known classes to classify unseen
classes. We find many approaches require intensive training augmentation with
attributes or features that may be commonly unavailable (attribute-based
learning) or susceptible to adversarial attacks (generative learning). We
propose combining approaches from the related person re-identification task for
ZSL, with key modifications to ensure sufficiently improved performance in the
ZSL setting without the need for feature or training dataset augmentation. We
are able to achieve state-of-the-art performance on the CUB200 and Cars196
datasets in the ZSL setting compared to recent works, with NMI (normalized
mutual inference) of 63.27 and top-1 of 61.04 for CUB200, and NMI 66.03 with
top-1 82.75% in Cars196. We also show state-of-the-art results in the
Generalized Zero-Shot Learning (GZSL) setting, with Harmonic Mean R-1 of 66.14%
on the CUB200 dataset.
