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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.
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Drew A. Hudson and Christopher D. Manning · 2019
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Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, and Marc’Aurelio Ranzato · 2019
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Language models are few-shot learners
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Independent prototype propagation for zero-shot compositionality
Frank Ruis, Gertjan J Burghouts, and Doina Bucur · 2021
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Training neural networks with fixed sparse masks
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Spot: Better frozen model adaptation through soft prompt transfer
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Multitask prompted training enables zero-shot task generalization
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