@inproceedings{1a7df284eca64d4a8b01246ec17ffadb,
title = "Diversity Measurement-Based Meta-Learning for Few-Shot Object Detection of Remote Sensing Images",
abstract = "Most object detection methods based on deep learning require large amounts of labeled data and can detect only the categories in the training set. Such issues significantly limit applications in remote sensing scenarios where it usually needs to recognize novel, unseen objects given very few training examples. To address these limitations, a novel meta-learning-based object detection method using Faster R-CNN framework is proposed for optical remote sensing image. Specifically, a diversity measurement module is proposed to measure diversity information between support images and query images on base classes so as to acquire more meta-knowledge. Experiments on DIOR dataset demonstrate our method has achieved superior performance than state-of-the-art meta-learning detection models in the field of remote sensing.",
keywords = "few-shot learning, meta-learning, Object detection, remote sensing images process",
author = "Lefan Wang and Shun Zhang and Zonghao Han and Yan Feng and Jiang Wei and Shaohui Mei",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884721",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3087--3090",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}