  Human-annotated attributes serve as powerful semantic embeddings in zero-shot
learning. However, their annotation process is labor-intensive and needs expert
supervision. Current unsupervised semantic embeddings, i.e., word embeddings,
enable knowledge transfer between classes. However, word embeddings do not
always reflect visual similarities and result in inferior zero-shot
performance. We propose to discover semantic embeddings containing
discriminative visual properties for zero-shot learning, without requiring any
human annotation. Our model visually divides a set of images from seen classes
into clusters of local image regions according to their visual similarity, and
further imposes their class discrimination and semantic relatedness. To
associate these clusters with previously unseen classes, we use external
knowledge, e.g., word embeddings and propose a novel class relation discovery
module. Through quantitative and qualitative evaluation, we demonstrate that
our model discovers semantic embeddings that model the visual properties of
both seen and unseen classes. Furthermore, we demonstrate on three benchmarks
that our visually-grounded semantic embeddings further improve performance over
word embeddings across various ZSL models by a large margin.
