  This work explores the zero-shot compositional learning ability of large
pre-trained vision-language models(VLMs) within the prompt-based learning
framework and propose a model (\textit{PromptCompVL}) to solve the compositonal
zero-shot learning (CZSL) problem. \textit{PromptCompVL} makes two design
choices: first, it uses a soft-prompting instead of hard-prompting to inject
learnable parameters to reprogram VLMs for compositional learning. Second, to
address the compositional challenge, it uses the soft-embedding layer to learn
primitive concepts in different combinations. By combining both soft-embedding
and soft-prompting, \textit{PromptCompVL} achieves state-of-the-art performance
on the MIT-States dataset. Furthermore, our proposed model achieves consistent
improvement compared to other CLIP-based methods which shows the effectiveness
of the proposed prompting strategies for CZSL.
