Few-shot object detection with feature attention highlight module in remote sensing images

Zixuan Xiao; Ping Zhong; Yuan Quan; Xuping Yin; Wei Xue

doi:10.1117/12.2577473

10 November 2020 Few-shot object detection with feature attention highlight module in remote sensing images

Zixuan Xiao, Ping Zhong, Yuan Quan, Xuping Yin, Wei Xue

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115840Z (2020) https://doi.org/10.1117/12.2577473
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

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.

Citation Download Citation

Zixuan Xiao, Ping Zhong, Yuan Quan, Xuping Yin, and Wei Xue "Few-shot object detection with feature attention highlight module in remote sensing images", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115840Z (10 November 2020); https://doi.org/10.1117/12.2577473

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