Text Semantic Fusion Relation Graph Reasoning for Few-Shot Object Detection on Remote Sensing Images

Most object detection methods based on remote sensing images are generally dependent on a large amount of high-quality labeled training data. However, due to the slow acquisition cycle of remote sensing images and the difficulty in labeling, many types of data samples are scarce. This makes few-shot...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1187
Main Authors: Zhang, Sanxing, Song, Fei, Liu, Xianyuan, Hao, Xuying, Liu, Yujia, Lei, Tao, Jiang, Ping
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2023
Subjects:
Algorithms
Artificial intelligence
Comparative analysis
data collection
Datasets
Deep learning
detectors
exhibitions
few-shot object detection
gated graph neural network (GGNN)
graphs
Image acquisition
image analysis
Knowledge
languages
Machine learning
Methods
Neural networks
Object recognition
Object recognition (Computers)
Pattern recognition
Reasoning
relation reasoning
Remote sensing
remote sensing images
Semantics
Sensors
spatial learning
Technology application
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Most object detection methods based on remote sensing images are generally dependent on a large amount of high-quality labeled training data. However, due to the slow acquisition cycle of remote sensing images and the difficulty in labeling, many types of data samples are scarce. This makes few-shot object detection an urgent and necessary research problem. In this paper, we introduce a remote sensing few-shot object detection method based on text semantic fusion relation graph reasoning (TSF-RGR), which learns various types of relationships from common sense knowledge in an end-to-end manner, thereby empowering the detector to reason over all classes. Specifically, based on the region proposals provided by the basic detection network, we first build a corpus containing a large number of text language descriptions, such as object attributes and relations, which are used to encode the corresponding common sense embeddings for each region. Then, graph structures are constructed between regions to propagate and learn key spatial and semantic relationships. Finally, a joint relation reasoning module is proposed to actively enhance the reliability and robustness of few-shot object feature representation by focusing on the degree of influence of different relations. Our TSF-RGR is lightweight and easy to expand, and it can incorporate any form of common sense information. Sufficient experiments show that the text information is introduced to deliver excellent performance gains for the baseline model. Compared with other few-shot detectors, the proposed method achieves state-of-the-art performance for different shot settings and obtains highly competitive results on two benchmark datasets (NWPU VHR-10 and DIOR).
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15051187