Zero-shot image recognition aims to classify data from unseen classes, by exploring the association between visual features and the semantic representations of each class. Most existing approaches focus on learning a shared single-scale embedding space (often at the output layer of the network) for both visual and semantic features, ignoring a fact that different-scale visual features exhibit different semantics. In this article, we propose a multi-scale visual-attribute co-attention (mVACA) model, considering both visual-semantic alignment and visual discrimination at multiple scales. At each scale, a hybrid visual attention is realized by attribute-related attention and visual self-attention. The attribute-related attention is guided by a pseudo attribute vector inferred via a mutual information regularization (MIR). The visual self-attentive features further influence the attribute attention to emphasize visual-associated attributes. Leveraging multiscale visual discrimination, mVACA unifies standard zero-shot learning (ZSL) and generalized ZSL tasks in one framework, achieving state-of-the-art or competitive performance on several commonly used benchmarks of both setups. To better understand the interaction between images and attributes in mVACA, we also provide visualized analysis.