  Pre-trained vision-language (V-L) models such as CLIP have shown excellent
generalization ability to downstream tasks. However, they are sensitive to the
choice of input text prompts and require careful selection of prompt templates
to perform well. Inspired by the Natural Language Processing (NLP) literature,
recent CLIP adaptation approaches learn prompts as the textual inputs to
fine-tune CLIP for downstream tasks. We note that using prompting to adapt
representations in a single branch of CLIP (language or vision) is sub-optimal
since it does not allow the flexibility to dynamically adjust both
representation spaces on a downstream task. In this work, we propose
Multi-modal Prompt Learning (MaPLe) for both vision and language branches to
improve alignment between the vision and language representations. Our design
promotes strong coupling between the vision-language prompts to ensure mutual
synergy and discourages learning independent uni-modal solutions. Further, we
learn separate prompts across different early stages to progressively model the
stage-wise feature relationships to allow rich context learning. We evaluate
the effectiveness of our approach on three representative tasks of
generalization to novel classes, new target datasets and unseen domain shifts.
Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable
performance and achieves an absolute gain of 3.45% on novel classes and 2.72%
on overall harmonic-mean, averaged over 11 diverse image recognition datasets.
Our code and pre-trained models are available at
https://github.com/muzairkhattak/multimodal-prompt-learning.
