  Prompt tuning is an effective way to adapt the pre-trained visual-language
model (VLM) to the downstream task using task-related textual tokens.
Representative CoOp-based work combines the learnable textual tokens with the
class tokens to obtain specific textual knowledge. However, the specific
textual knowledge is the worse generalization to the unseen classes because it
forgets the essential general textual knowledge having a strong generalization
ability. To tackle this issue, we introduce a novel Knowledge-guided Context
Optimization (KgCoOp) to enhance the generalization ability of the learnable
prompt for unseen classes. The key insight of KgCoOp is that forgetting about
essential knowledge can be alleviated by reducing the discrepancy between the
learnable prompt and the hand-crafted prompt. Especially, KgCoOp minimizes the
discrepancy between the textual embeddings generated by learned prompts and the
hand-crafted prompts. Finally, adding the KgCoOp upon the contrastive loss can
make a discriminative prompt for both seen and unseen tasks. Extensive
evaluation of several benchmarks demonstrates that the proposed
Knowledge-guided Context Optimization is an efficient method for prompt tuning,
\emph{i.e.,} achieves better performance with less training time.
