SAMS : One-Shot Learning for the Segment Anything Model Using Similar Images-Reference-Cited by-同舟云学术

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SAMS : One-Shot Learning for the Segment Anything Model Using Similar Images

Published:2024-06-30 Issue: Volume:21 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Yin Fan¹,Li Jun¹,Wei Yifei¹,Zhang Wei²,Xu Chaoyi¹

Affiliation:

1. Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China,100876

2. General Research Institude Ltd.,China Datang Corporation Science and Technology,Beijing,China,100040

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651474.pdf?arnumber=10651474

Reference38 articles.

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Devlin,2019

2. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer;Raffel;J. Mach. Learn. Res.,2020

3. GPT-4 Technical Report,2023

4. LLaMA: Open and Efficient Foundation Language Models;Touvron,2023

5. Language Models are Few-Shot Learners;Brown,2020

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