Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

TL;DR

This paper introduces GE-FSOD, a novel model that enhances generalization in remote sensing few-shot object detection through multi-scale feature fusion, refined region proposals, and improved classification, achieving state-of-the-art results.

Contribution

The paper presents a new model with three innovations: CFPAN, MRRPN, and GCL, specifically designed to improve generalization in remote sensing FSOD tasks.

Findings

Achieves state-of-the-art performance on DIOR and NWPU VHR-10 datasets.

Outperforms existing FSOD methods in remote sensing scenarios.

Demonstrates robustness in detecting novel and diverse objects.

Abstract

Remote sensing object detection is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our model achieves state-of-the-art performance for few-shot object detection in remote sensing.