  Zero-shot learning (ZSL) aims to recognize classes that do not have samples
in the training set. One representative solution is to directly learn an
embedding function associating visual features with corresponding class
semantics for recognizing new classes. Many methods extend upon this solution,
and recent ones are especially keen on extracting rich features from images,
e.g. attribute features. These attribute features are normally extracted within
each individual image; however, the common traits for features across images
yet belonging to the same attribute are not emphasized. In this paper, we
propose a new framework to boost ZSL by explicitly learning attribute
prototypes beyond images and contrastively optimizing them with attribute-level
features within images. Besides the novel architecture, two elements are
highlighted for attribute representations: a new prototype generation module is
designed to generate attribute prototypes from attribute semantics; a hard
example-based contrastive optimization scheme is introduced to reinforce
attribute-level features in the embedding space. We explore two alternative
backbones, CNN-based and transformer-based, to build our framework and conduct
experiments on three standard benchmarks, CUB, SUN, AwA2. Results on these
benchmarks demonstrate that our method improves the state of the art by a
considerable margin. Our codes will be available at
https://github.com/dyabel/CoAR-ZSL.git
