In this paper, we consider ZSL in the case where both visual and linguistic concepts are represented by Gaussian distribution embeddings.
our Gaussian-embedding approach to ZSL learns concept distributions in both domains: Gaussians representing individual words (as in (Vilnis and McCallum, 2015)) and Gaussians representing visual concepts.