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Computer Science > Machine Learning

arXiv:2303.02506 (cs)
[Submitted on 4 Mar 2023 (v1), last revised 18 Jan 2024 (this version, v3)]

Title:Prismer: A Vision-Language Model with Multi-Task Experts

Authors:Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar
View a PDF of the paper titled Prismer: A Vision-Language Model with Multi-Task Experts, by Shikun Liu and 5 other authors
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Abstract:Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of task-specific experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from multiple readily-available, pre-trained experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show Prismer can efficiently pool this expert knowledge and adapt it to various vision-language reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned and few-shot learning performance which is competitive with current state-of-the-arts, whilst requiring up to two orders of magnitude less training data. Code is available at this https URL.
Comments: Published at TMLR 2024. Project Page: this https URL Code: this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2303.02506 [cs.LG]
  (or arXiv:2303.02506v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2303.02506
arXiv-issued DOI via DataCite

Submission history

From: Shikun Liu [view email]
[v1] Sat, 4 Mar 2023 21:22:47 UTC (12,817 KB)
[v2] Sun, 12 Mar 2023 02:30:16 UTC (12,817 KB)
[v3] Thu, 18 Jan 2024 22:09:40 UTC (12,820 KB)
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