  We study zero-shot learning (ZSL) as a transfer learning problem, and focus
on the two key aspects of ZSL, model effectiveness and model adaptation. For
effective modeling, we adopt the boosting strategy to learn a zero-shot
classifier from weak models to a strong model. For adaptable knowledge
transfer, we devise a Semantic Correlation Regularization (SCR) approach to
regularize the boosted model to be consistent with the inter-class semantic
correlations. With SCR embedded in the boosting objective, and with a
self-controlled sample selection for learning robustness, we propose a unified
framework, Boosted Zero-shot classification with Semantic Correlation
Regularization (BZ-SCR). By balancing the SCR-regularized boosted model
selection and the self-controlled sample selection, BZ-SCR is capable of
capturing both discriminative and adaptable feature-to-class semantic
alignments, while ensuring the reliability and adaptability of the learned
samples. The experiments on two ZSL datasets show the superiority of BZ-SCR
over the state-of-the-arts.
