Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models-Reference-Cited by-同舟云学术

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Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models

Published:2023-10-01 Issue: Volume:35 Page:15624-15634
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Kan Baoshuo¹,Wang Teng²,Lu Wenpeng¹,Zhen Xiantong³,Guan Weili⁴,Zheng Feng²

Affiliation:

1. Qilu University of Technology (Shandong Academy of Sciences)

2. Southern University of Science and Technology

3. United Imaging Healthcare

4. Monash Univerisity

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376778.pdf?arnumber=10376778

Reference49 articles.

1. Visual prompting via image inpainting;Bar;Advances in Neural Information Processing Systems,2022

2. Food-101 – Mining Discriminative Components with Random Forests

3. Language models are few-shot learners;Brown;Advances in Neural Information Processing Systems,2020

4. KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

5. UNITER: UNiversal Image-TExt Representation Learning

Cited by 2 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. Learning emotional prompt features with multiple views for visual emotion analysis;Information Fusion;2024-08

2. Learning to Supervise Knowledge Retrieval Over a Tree Structure for Visual Question Answering;IEEE Transactions on Multimedia;2024

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