Few-Shot Object Detection Based on Contrastive Class-Attention Feature Reweighting for Remote Sensing Images
Wang Miao,
Zihao Zhao,
Jie Geng
et al.
Abstract:Remote sensing image object detection with deep neural networks has been highly successful, but it heavily relies on a large number of labeled samples for optimal performance. Unfortunately, when faced with limited labeled samples, the performance of object detection deteriorates. In order to overcome these limitations, we propose a few-shot object detection (FSOD) method based on the reweighting of contrastive class-attention features for remote sensing images. A Siamese representation embedding model based o… Show more
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