{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:03Z","timestamp":1747216143950,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685489"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Few-shot learning is crucial for downstream tasks involving point clouds, given the challenge of obtaining sufficient datasets due to extensive collecting and labeling efforts. Pre-trained VLM-Guided point cloud models, containing abundant knowledge, can compensate for the scarcity of training data, potentially leading to very good performance. However, adapting these pre-trained point cloud models to specific few-shot learning tasks is challenging due to their huge number of parameters and high computational cost. To this end, we propose a novel Dynamic Multimodal Prompt Tuning method, named DMMPT, for boosting few-shot learning with pre-trained VLM-Guided point cloud models. Specifically, we build a dynamic knowledge collector capable of gathering task- and data-related information from various modalities. Then, a multimodal prompt generator is constructed to integrate collected dynamic knowledge and generate multimodal prompts, which efficiently direct pre-trained VLM-guided point cloud models toward few-shot learning tasks and address the issue of limited training data. Our method is evaluated on benchmark datasets not only in a standard N-way K-shot few-shot learning setting, but also in a more challenging setting with all classes and K-shot few-shot learning. Notably, our method outperforms other prompt-tuning techniques, achieving highly competitive results comparable to full fine-tuning methods while significantly enhancing computational efficiency.<\/jats:p>","DOI":"10.3233\/faia240559","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:49:38Z","timestamp":1729169378000},"source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Multimodal Prompt Tuning: Boost Few-Shot Learning with VLM-Guided Point Cloud Models"],"prefix":"10.3233","author":[{"given":"Xiang","family":"Gu","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Nanjing University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5668-833X","authenticated-orcid":false,"given":"Shuchao","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Nanjing University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-3125","authenticated-orcid":false,"given":"Anan","family":"Du","sequence":"additional","affiliation":[{"name":"Nanjing Vocational University of Industry Technology, China"}]},{"given":"Yifei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Nanjing University of Science and Technology, China"}]},{"given":"Jixiang","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Nanjing University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-2441","authenticated-orcid":false,"given":"Jorge","family":"D\u00edez","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, University of Oviedo at Gij\u00f3n, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240559","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:49:38Z","timestamp":1729169378000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240559","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}