  With the growing interest in pretrained vision-language models like CLIP,
recent research has focused on adapting these models to downstream tasks.
Despite achieving promising results, most existing methods require labeled data
for all classes, which may not hold in real-world applications due to the long
tail and Zipf's law. For example, some classes may lack labeled data entirely,
such as emerging concepts. To address this problem, we propose a plug-and-play
generative approach called \textbf{S}ynt\textbf{H}es\textbf{I}zed
\textbf{P}rompts~(\textbf{SHIP}) to improve existing fine-tuning methods.
Specifically, we follow variational autoencoders to introduce a generator that
reconstructs the visual features by inputting the synthesized prompts and the
corresponding class names to the textual encoder of CLIP. In this manner, we
easily obtain the synthesized features for the remaining label-only classes.
Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled
and synthesized features. Extensive experiments on base-to-new generalization,
cross-dataset transfer learning, and generalized zero-shot learning demonstrate
the superiority of our approach. The code is available at
\url{https://github.com/mrflogs/SHIP}.
