Improving Few-Shot Object Detection through a Performance Analysis on Aerial and Natural Images
Author:
Affiliation:
1. Université Sorbonne Paris Nord,COSE & L2TI
2. L2TI Université Sorbonne Paris Nord
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9908519/9909515/09909878.pdf?arnumber=9909878
Reference11 articles.
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3. Fcos: Fully con-volutional one-stage object detection;tian;Proceedings of the IEEE/CVF International Conference on Computer Vision,2019
4. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
5. Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
Cited by 3 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. Improving Few-Shot and Cross-Domain Object Detection on Aerial Images with a Diffusion-Based Detector;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. Unlocking the capabilities of explainable few-shot learning in remote sensing;Artificial Intelligence Review;2024-06-10
3. Vision Based Few-Shot Object Detection;2024 5th International Conference for Emerging Technology (INCET);2024-05-24
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