A survey of efficient fine-tuning methods for Vision-Language Models — Prompt and Adapter-Reference-Cited by-同舟云学术

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A survey of efficient fine-tuning methods for Vision-Language Models — Prompt and Adapter

Published:2024-04 Issue: Volume:119 Page:103885
ISSN:0097-8493
Container-title:Computers & Graphics
language:en
Short-container-title:Computers & Graphics

Author:

Xing Jialu,Liu Jianping,Wang Jian,Sun Lulu,Chen Xi,Gu Xunxun,Wang Yingfei

Publisher

Elsevier BV

Reference117 articles.

1. Learning transferable visual models from natural language supervision;Radford,2021

2. PaLM-E: An embodied multimodal language model;Driess,2023

3. Language models are few-shot learners;Brown,2020

4. LLaMA: Open and efficient foundation language models;Touvron,2023

5. Parameter-efficient transfer learning for NLP;Houlsby,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Editorial note Computers & Graphics issue 119;Computers & Graphics;2024-04

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