  Few-shot unsupervised domain adaptation (FS-UDA) utilizes few-shot labeled
source domain data to realize effective classification in unlabeled target
domain. However, current FS-UDA methods are still suffer from two issues: 1)
the data from different domains can not be effectively aligned by few-shot
labeled data due to the large domain gaps, 2) it is unstable and time-consuming
to generalize to new FS-UDA tasks.To address this issue, we put forward a novel
Efficient Meta Prompt Learning Framework for FS-UDA. Within this framework, we
use pre-trained CLIP model as the feature learning base model. First, we design
domain-shared prompt learning vectors composed of virtual tokens, which mainly
learns the meta knowledge from a large number of meta tasks to mitigate domain
gaps. Secondly, we also design a task-shared prompt learning network to
adaptively learn specific prompt vectors for each task, which aims to realize
fast adaptation and task generalization. Thirdly, we learn a task-specific
cross-domain alignment projection and a task-specific classifier with
closed-form solutions for each meta task, which can efficiently adapt the model
to new tasks in one step. The whole learning process is formulated as a bilevel
optimization problem, and a good initialization of model parameters is learned
through meta-learning. Extensive experimental study demonstrates the promising
performance of our framework on benchmark datasets. Our method has the large
improvement of at least 15.4% on 5-way 1-shot and 8.7% on 5-way 5-shot,
compared with the state-of-the-art methods. Also, the performance of our method
on all the test tasks is more stable than the other methods.
