Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images

• URL: http://arxiv.org/abs/2009.01616v1
• Date: Thu, 3 Sep 2020 12:38:49 GMT
• Title: Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images
• Authors: Zixuan Xiao, Ping Zhong, Yuan Quan, Xuping Yin, Wei Xue
• Abstract summary: We propose a few-shot object detector which is designed for detecting novel objects based on only a few examples. Our model is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend. Experiments demonstrate the effectiveness of the proposed method for few-shot cases.
• Score: 10.92844145381214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely rare. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects based on only a few examples. Through fully leveraging labeled base classes, our model that is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend can quickly adapt to novel classes. The pre-trained feature extractor whose parameters are shared produces general features. While the feature attention highlight module is designed to be light-weighted and simple in order to fit the few-shot cases. Although it is simple, the information provided by it in a serial way is helpful to make the general features to be specific for few-shot objects. Then the object-specific features are delivered to the two-stage detection backend for the detection results. The experiments demonstrate the effectiveness of the proposed method for few-shot cases.

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