Dynamic multimodal prompt tuning: boost few-shot learning with vlm-guided point cloud models

dc.contributor.author	Gu, X.
dc.contributor.author	Pang, S.
dc.contributor.author	Du, A.
dc.contributor.author	Wang, Y.
dc.contributor.author	Miao, J.
dc.contributor.author	Díez Peláez, Jorge
dc.date.accessioned	2025-03-31T11:48:20Z
dc.date.available	2025-03-31T11:48:20Z
dc.date.issued	2024
dc.identifier.isbn	978-164368548-9
dc.identifier.issn	0922-6389
dc.identifier.uri	https://hdl.handle.net/10651/78399
dc.format.extent	p. 761-768
dc.language.iso	eng
dc.relation.ispartof	Frontiers in Artificial Intelligence and Applications
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213357384&doi=10.3233%2fFAIA240559&partnerID=40&md5=f6576ca74b75f13064c912fb1e2f4c10
dc.title	Dynamic multimodal prompt tuning: boost few-shot learning with vlm-guided point cloud models
dc.type	conference output
dc.identifier.doi	10.3233/FAIA240559
dc.relation.publisherversion	http://dx.doi.org/10.3233/FAIA240559

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