  The purpose of few-shot recognition is to recognize novel categories with a
limited number of labeled examples in each class. To encourage learning from a
supplementary view, recent approaches have introduced auxiliary semantic
modalities into effective metric-learning frameworks that aim to learn a
feature similarity between training samples (support set) and test samples
(query set). However, these approaches only augment the representations of
samples with available semantics while ignoring the query set, which loses the
potential for the improvement and may lead to a shift between the modalities
combination and the pure-visual representation. In this paper, we devise an
attributes-guided attention module (AGAM) to utilize human-annotated attributes
and learn more discriminative features. This plug-and-play module enables
visual contents and corresponding attributes to collectively focus on important
channels and regions for the support set. And the feature selection is also
achieved for query set with only visual information while the attributes are
not available. Therefore, representations from both sets are improved in a
fine-grained manner. Moreover, an attention alignment mechanism is proposed to
distill knowledge from the guidance of attributes to the pure-visual branch for
samples without attributes. Extensive experiments and analysis show that our
proposed module can significantly improve simple metric-based approaches to
achieve state-of-the-art performance on different datasets and settings.
