  We study the problem of compositional zero-shot learning for object-attribute
recognition. Prior works use visual features extracted with a backbone network,
pre-trained for object classification and thus do not capture the subtly
distinct features associated with attributes. To overcome this challenge, these
studies employ supervision from the linguistic space, and use pre-trained word
embeddings to better separate and compose attribute-object pairs for
recognition. Analogous to linguistic embedding space, which already has unique
and agnostic embeddings for object and attribute, we shift the focus back to
the visual space and propose a novel architecture that can disentangle
attribute and object features in the visual space. We use visual decomposed
features to hallucinate embeddings that are representative for the seen and
novel compositions to better regularize the learning of our model. Extensive
experiments show that our method outperforms existing work with significant
margin on three datasets: MIT-States, UT-Zappos, and a new benchmark created
based on VAW. The code, models, and dataset splits are publicly available at
https://github.com/nirat1606/OADis.
