Few-Shot Object Detection for Remote Sensing Images via Pseudo-Sample Generation and Feature Enhancement
Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. However, existing methods mainly focus on natural images and overlook the scale variations in remote sensing images, leading to feature confusion among foreground instances of different cl...
Saved in:
| Main Authors: | Zhaoguo Huang, Danyang Chen, Cheng Zhong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4477 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Modal Prototypes for Few-Shot Object Detection in Remote Sensing Images
by: Yanxing Liu, et al.
Published: (2024-12-01) -
Complementary Local–Global Optimization for Few-Shot Object Detection in Remote Sensing
by: Yutong Zhang, et al.
Published: (2025-06-01) -
Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification
by: Liu Wang, et al.
Published: (2025-01-01) -
SEMPNet: enhancing few-shot remote sensing image semantic segmentation through the integration of the segment anything model
by: Wei Ao, et al.
Published: (2024-12-01) -
Few-Shot Object Detection via Sample Processing
by: Honghui Xu, et al.
Published: (2021-01-01)