  Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen
classes by training only the seen classes, in which the instances of unseen
classes tend to be biased towards the seen class. In this paper, we propose a
Cluster-based Contrastive Disentangling (CCD) method to improve GZSL by
alleviating the semantic gap and domain shift problems. Specifically, we first
cluster the batch data to form several sets containing similar classes. Then,
we disentangle the visual features into semantic-unspecific and
semantic-matched variables, and further disentangle the semantic-matched
variables into class-shared and class-unique variables according to the
clustering results. The disentangled learning module with random swapping and
semantic-visual alignment bridges the semantic gap. Moreover, we introduce
contrastive learning on semantic-matched and class-unique variables to learn
high intra-set and intra-class similarity, as well as inter-set and inter-class
discriminability. Then, the generated visual features conform to the underlying
characteristics of general images and have strong discriminative information,
which alleviates the domain shift problem well. We evaluate our proposed method
on four datasets and achieve state-of-the-art results in both conventional and
generalized settings.
