  Current approaches to Zero-Shot Learning (ZSL) struggle to learn
generalizable semantic knowledge capable of capturing complex correlations.
Inspired by \emph{Spiral Curriculum}, which enhances learning processes by
revisiting knowledge, we propose a form of spiral learning which revisits
visual representations based on a sequence of attribute groups (e.g., a
combined group of \emph{color} and \emph{shape}). Spiral learning aims to learn
generalized local correlations, enabling models to gradually enhance global
learning and thus understand complex correlations. Our implementation is based
on a 2-stage \emph{Reinforced Self-Revised (RSR)} framework: \emph{preview} and
\emph{review}. RSR first previews visual information to construct diverse
attribute groups in a weakly-supervised manner. Then, it spirally learns
refined localities based on attribute groups and uses localities to revise
global semantic correlations. Our framework outperforms state-of-the-art
algorithms on four benchmark datasets in both zero-shot and generalized
zero-shot settings, which demonstrates the effectiveness of spiral learning in
learning generalizable and complex correlations. We also conduct extensive
analysis to show that attribute groups and reinforced decision processes can
capture complementary semantic information to improve predictions and aid
explainability.
