Learning to Prompt for Vision-Language Models
Learning to Prompt for Vision-Language Models
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, wh...
Alternative Titles
Full title
Learning to Prompt for Vision-Language Models
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TN_cdi_proquest_journals_2701324259
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2701324259
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ISSN
0920-5691
E-ISSN
1573-1405
DOI
10.1007/s11263-022-01653-1
