  Zero-shot learning (ZSL) aims to recognize novel classes by transferring
semantic knowledge from seen classes to unseen ones. Semantic knowledge is
learned from attribute descriptions shared between different classes, which act
as strong priors for localizing object attributes that represent discriminative
region features, enabling significant visual-semantic interaction. Although
some attention-based models have attempted to learn such region features in a
single image, the transferability and discriminative attribute localization of
visual features are typically neglected. In this paper, we propose an
attribute-guided Transformer network, termed TransZero, to refine visual
features and learn attribute localization for discriminative visual embedding
representations in ZSL. Specifically, TransZero takes a feature augmentation
encoder to alleviate the cross-dataset bias between ImageNet and ZSL
benchmarks, and improves the transferability of visual features by reducing the
entangled relative geometry relationships among region features. To learn
locality-augmented visual features, TransZero employs a visual-semantic decoder
to localize the image regions most relevant to each attribute in a given image,
under the guidance of semantic attribute information. Then, the
locality-augmented visual features and semantic vectors are used to conduct
effective visual-semantic interaction in a visual-semantic embedding network.
Extensive experiments show that TransZero achieves the new state of the art on
three ZSL benchmarks. The codes are available at:
\url{https://github.com/shiming-chen/TransZero}.
