  Image-language models with prompt learning have shown remarkable advances in
numerous downstream vision tasks. Nevertheless, conventional prompt learning
methods overfit their training distribution and lose the generalization ability
on test distributions. To improve generalization across various distribution
shifts, we propose any-shift prompting: a general probabilistic inference
framework that considers the relationship between training and test
distributions during prompt learning. We explicitly connect training and test
distributions in the latent space by constructing training and test prompts in
a hierarchical architecture. Within this framework, the test prompt exploits
the distribution relationships to guide the generalization of the CLIP
image-language model from training to any test distribution. To effectively
encode the distribution information and their relationships, we further
introduce a transformer inference network with a pseudo-shift training
mechanism. The network generates the tailored test prompt with both training
and test information in a feedforward pass, avoiding extra training costs at
test time. Extensive experiments on twenty-three datasets demonstrate the
effectiveness of any-shift prompting on the generalization over various
distribution shifts.
