  Few-shot learning is a challenging problem since only a few examples are
provided to recognize a new class. Several recent studies exploit additional
semantic information, e.g. text embeddings of class names, to address the issue
of rare samples through combining semantic prototypes with visual prototypes.
However, these methods still suffer from the spurious visual features learned
from the rare support samples, resulting in limited benefits. In this paper, we
propose a novel Semantic Prompt (SP) approach for few-shot learning. Instead of
the naive exploitation of semantic information for remedying classifiers, we
explore leveraging semantic information as prompts to tune the visual feature
extraction network adaptively. Specifically, we design two complementary
mechanisms to insert semantic prompts into the feature extractor: one is to
enable the interaction between semantic prompts and patch embeddings along the
spatial dimension via self-attention, another is to supplement visual features
with the transformed semantic prompts along the channel dimension. By combining
these two mechanisms, the feature extractor presents a better ability to attend
to the class-specific features and obtains more generalized image
representations with merely a few support samples. Through extensive
experiments on four datasets, the proposed approach achieves promising results,
improving the 1-shot learning accuracy by 3.67% on average.
