  Zero-shot learning (ZSL) tackles the novel class recognition problem by
transferring semantic knowledge from seen classes to unseen ones. Existing
attention-based models have struggled to learn inferior region features in a
single image by solely using unidirectional attention, which ignore the
transferability and discriminative attribute localization of visual features.
In this paper, we propose a cross attribute-guided Transformer network, termed
TransZero++, to refine visual features and learn accurate attribute
localization for semantic-augmented visual embedding representations in ZSL.
TransZero++ consists of an attribute$\rightarrow$visual Transformer sub-net
(AVT) and a visual$\rightarrow$attribute Transformer sub-net (VAT).
Specifically, AVT first takes a feature augmentation encoder to alleviate the
cross-dataset problem, and improves the transferability of visual features by
reducing the entangled relative geometry relationships among region features.
Then, an attribute$\rightarrow$visual decoder is employed to localize the image
regions most relevant to each attribute in a given image for attribute-based
visual feature representations. Analogously, VAT uses the similar feature
augmentation encoder to refine the visual features, which are further applied
in visual$\rightarrow$attribute decoder to learn visual-based attribute
features. By further introducing semantical collaborative losses, the two
attribute-guided transformers teach each other to learn semantic-augmented
visual embeddings via semantical collaborative learning. Extensive experiments
show that TransZero++ achieves the new state-of-the-art results on three
challenging ZSL benchmarks. The codes are available at:
\url{https://github.com/shiming-chen/TransZero_pp}.
