  Learning a common latent embedding by aligning the latent spaces of
cross-modal autoencoders is an effective strategy for Generalized Zero-Shot
Classification (GZSC). However, due to the lack of fine-grained instance-wise
annotations, it still easily suffer from the domain shift problem for the
discrepancy between the visual representation of diversified images and the
semantic representation of fixed attributes. In this paper, we propose an
innovative autoencoder network by learning Aligned Cross-Modal Representations
(dubbed ACMR) for GZSC. Specifically, we propose a novel Vision-Semantic
Alignment (VSA) method to strengthen the alignment of cross-modal latent
features on the latent subspaces guided by a learned classifier. In addition,
we propose a novel Information Enhancement Module (IEM) to reduce the
possibility of latent variables collapse meanwhile encouraging the
discriminative ability of latent variables. Extensive experiments on publicly
available datasets demonstrate the state-of-the-art performance of our method.
