English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

Learning to Compose Soft Prompts for Compositional Zero-Shot Learning