RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model
Author:
Affiliation:
1. Image Processing Center, School of Astronautics, Beihang University, Beijing, China
2. Shanghai Artificial Intelligence Laboratory, Shanghai, China
3. Department of Geography, The University of Hong Kong, Hong Kong, 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)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10409216.pdf?arnumber=10409216
Reference98 articles.
1. HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery
2. Object Detection and Instance Segmentation in Remote Sensing Imagery Based on Precise Mask R-CNN
3. Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
4. An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation
5. Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
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