  Humans can obtain the knowledge of novel visual concepts from language
descriptions, and we thus use the few-shot image classification task to
investigate whether a machine learning model can have this capability. Our
proposed model, LIDE (Learning from Image and DEscription), has a text decoder
to generate the descriptions and a text encoder to obtain the text
representations of machine- or user-generated descriptions. We confirmed that
LIDE with machine-generated descriptions outperformed baseline models.
Moreover, the performance was improved further with high-quality user-generated
descriptions. The generated descriptions can be viewed as the explanations of
the model's predictions, and we observed that such explanations were consistent
with prediction results. We also investigated why the language description
improved the few-shot image classification performance by comparing the image
representations and the text representations in the feature spaces.
