  Zero-Shot Learning (ZSL) aims to recognise unseen object classes, which are
not observed during the training phase. The existing body of works on ZSL
mostly relies on pretrained visual features and lacks the explicit attribute
localisation mechanism on images. In this work, we propose an attention-based
model in the problem settings of ZSL to learn attributes useful for unseen
class recognition. Our method uses an attention mechanism adapted from Vision
Transformer to capture and learn discriminative attributes by splitting images
into small patches. We conduct experiments on three popular ZSL benchmarks
(i.e., AWA2, CUB and SUN) and set new state-of-the-art harmonic mean results
{on all the three datasets}, which illustrate the effectiveness of our proposed
method.
