  Few-shot learning aims to generalize the recognizer from seen categories to
an entirely novel scenario. With only a few support samples, several advanced
methods initially introduce class names as prior knowledge for identifying
novel classes. However, obstacles still impede achieving a comprehensive
understanding of how to harness the mutual advantages of visual and textual
knowledge. In this paper, we propose a coherent Bidirectional Knowledge
Permeation strategy called BiKop, which is grounded in a human intuition: A
class name description offers a general representation, whereas an image
captures the specificity of individuals. BiKop primarily establishes a
hierarchical joint general-specific representation through bidirectional
knowledge permeation. On the other hand, considering the bias of joint
representation towards the base set, we disentangle base-class-relevant
semantics during training, thereby alleviating the suppression of potential
novel-class-relevant information. Experiments on four challenging benchmarks
demonstrate the remarkable superiority of BiKop. Our code will be publicly
available.
