  Generalized zero-shot learning (GZSL) aims to recognize objects from both
seen and unseen classes, when only the labeled examples from seen classes are
provided. Recent feature generation methods learn a generative model that can
synthesize the missing visual features of unseen classes to mitigate the
data-imbalance problem in GZSL. However, the original visual feature space is
suboptimal for GZSL classification since it lacks discriminative information.
To tackle this issue, we propose to integrate the generation model with the
embedding model, yielding a hybrid GZSL framework. The hybrid GZSL approach
maps both the real and the synthetic samples produced by the generation model
into an embedding space, where we perform the final GZSL classification.
Specifically, we propose a contrastive embedding (CE) for our hybrid GZSL
framework. The proposed contrastive embedding can leverage not only the
class-wise supervision but also the instance-wise supervision, where the latter
is usually neglected by existing GZSL researches. We evaluate our proposed
hybrid GZSL framework with contrastive embedding, named CE-GZSL, on five
benchmark datasets. The results show that our CEGZSL method can outperform the
state-of-the-arts by a significant margin on three datasets. Our codes are
available on https://github.com/Hanzy1996/CE-GZSL.

Computer Vision and Pattern Recognition;Artificial Intelligence