  We introduce compositional soft prompting (CSP), a parameter-efficient
learning technique to improve the zero-shot compositionality of large-scale
pretrained vision-language models (VLMs) like CLIP. We develop CSP for
compositional zero-shot learning, the task of predicting unseen
attribute-object compositions (e.g., old cat and young tiger). VLMs have a
flexible text encoder that can represent arbitrary classes as natural language
prompts but they often underperform task-specific architectures on the
compositional zero-shot benchmark datasets. CSP treats the attributes and
objects that define classes as learnable tokens of vocabulary. During training,
the vocabulary is tuned to recognize classes that compose tokens in multiple
ways (e.g., old cat and white cat). At test time, we recompose the learned
attribute-object vocabulary in new combinations to recognize novel classes. We
show that CSP outperforms the CLIP on benchmark datasets by an average of 10.9
percentage points on AUC. CSP also outperforms CoOp, a soft prompting method
that fine-tunes the prefix context tokens, by an average of 5.8 percentage
points on AUC. We perform additional experiments to show that CSP improves
generalization to higher-order attribute-attribute-object compositions (e.g.,
old white cat) and combinations of pretrained attributes and fine-tuned
objects. The code is available at https://github.com/BatsResearch/csp.
