A Decoupling Paradigm With Prompt Learning for Remote Sensing Image Change Captioning-Reference-Cited by-同舟云学术

A Decoupling Paradigm With Prompt Learning for Remote Sensing Image Change Captioning

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Chenyang¹^ORCID,Zhao Rui²^ORCID,Chen Jianqi¹^ORCID,Qi Zipeng¹^ORCID,Zou Zhengxia³^ORCID,Shi Zhenwei¹^ORCID

Affiliation:

1. Image Processing Center, School of Astronautics, the Beijing Key Laboratory of Digital Media, and the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

2. Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore

3. Department of Guidance, Navigation and Control, School of Astronautics, Beihang University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10271701.pdf?arnumber=10271701

Reference89 articles.

1. From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques

2. Prefix-Tuning: Optimizing Continuous Prompts for Generation

3. SIAMESE NETWORK WITH MULTI-LEVEL FEATURES FOR PATCH-BASED CHANGE DETECTION IN SATELLITE IMAGERY

4. Universal Adversarial Triggers for Attacking and Analyzing NLP

5. DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images;chen;IEEE J Sel Topics Appl Earth Observ Remote Sens,2021

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