  Pre-trained vision-language models, e.g., CLIP, working with manually
designed prompts have demonstrated great capacity of transfer learning.
Recently, learnable prompts achieve state-of-the-art performance, which however
are prone to overfit to seen classes, failing to generalize to unseen classes.
In this paper, we propose a Knowledge-Aware Prompt Tuning (KAPT) framework for
vision-language models. Our approach takes inspiration from human intelligence
in which external knowledge is usually incorporated into recognizing novel
categories of objects. Specifically, we design two complementary types of
knowledge-aware prompts for the text encoder to leverage the distinctive
characteristics of category-related external knowledge. The discrete prompt
extracts the key information from descriptions of an object category, and the
learned continuous prompt captures overall contexts. We further design an
adaptation head for the visual encoder to aggregate salient attentive visual
cues, which establishes discriminative and task-aware visual representations.
We conduct extensive experiments on 11 widely-used benchmark datasets and the
results verify the effectiveness in few-shot image classification, especially
in generalizing to unseen categories. Compared with the state-of-the-art CoCoOp
method, KAPT exhibits favorable performance and achieves an absolute gain of
3.22% on new classes and 2.57% in terms of harmonic mean.
