  Generalized compositional zero-shot learning means to learn composed concepts
of attribute-object pairs in a zero-shot fashion, where a model is trained on a
set of seen concepts and tested on a combined set of seen and unseen concepts.
This task is very challenging because of not only the gap between seen and
unseen concepts but also the contextual dependency between attributes and
objects. This paper introduces a new approach, termed translational concept
embedding, to solve these two difficulties in a unified framework. It models
the effect of applying an attribute to an object as adding a translational
attribute feature to an object prototype. We explicitly take into account of
the contextual dependency between attributes and objects by generating
translational attribute features conditionally dependent on the object
prototypes. Furthermore, we design a ratio variance constraint loss to promote
the model's generalization ability on unseen concepts. It regularizes the
distances between concepts by utilizing knowledge from their pretrained word
embeddings. We evaluate the performance of our model under both the unbiased
and biased concept classification tasks, and show that our model is able to
achieve good balance in predicting unseen and seen concepts.
