Few-shot Object Detection on Remote Sensing Images

Abstract

In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote sensing images and the report remarkable achievements in detection performance and efficiency. However, current CNN-based methods mostly require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories. More specifically, our model contains three main components: a meta feature extractor that learns to extract feature representations from input images, a reweighting module that learn to adaptively assign different weights for each feature representation from the support images, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework. Experiments on two benchmark datasets demonstrate that with only a few annotated samples our model can still achieve a satisfying detection performance on remote sensing images and the performance of our model is significantly better than the well-established baseline models.

arXiv comment12pages, 7 figures

References in corpus (9)

• Prototypical Networks for Few-shot Learning
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
• DSSD : Deconvolutional Single Shot Detector
• A Survey on Object Detection in Optical Remote Sensing Images
• Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
• Few-Example Object Detection with Model Communication
• Learning a Rotation Invariant Detector with Rotatable Bounding Box
• LSTD: A Low-Shot Transfer Detector for Object Detection
• Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification

Cited by in corpus (3)

• G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
• Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
• Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images