  Generalized zero-shot learning (GZSL) is a technique to train a deep learning
model to identify unseen classes using the image attribute. In this paper, we
put forth a new GZSL approach exploiting Vision Transformer (ViT) to maximize
the attribute-related information contained in the image feature. In ViT, the
entire image region is processed without the degradation of the image
resolution and the local image information is preserved in patch features. To
fully enjoy these benefits of ViT, we exploit patch features as well as the CLS
feature in extracting the attribute-related image feature. In particular, we
propose a novel attention-based module, called attribute attention module
(AAM), to aggregate the attribute-related information in patch features. In
AAM, the correlation between each patch feature and the synthetic image
attribute is used as the importance weight for each patch. From extensive
experiments on benchmark datasets, we demonstrate that the proposed technique
outperforms the state-of-the-art GZSL approaches by a large margin.
