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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Dengyong Zhang1,2, Huaijian Pu1,2, Feng Li1,2,*, Xiangling Ding3, Victor S. Sheng4

1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
3 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411004, China
4 Department of Computer Science, Texas Tech University, Lubbock, 79409, TX, USA

* Corresponding Author: Feng Li. Email: email

Computers, Materials & Continua 2023, 74(2), 3439-3454.


Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are used to make the network focus on features that are more useful to the object. In addition, the recently popular transformer is used to fuse the features of the existing object. This compensates for the previous network failure by making full use of existing object features. Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.


Cite This Article

D. Zhang, H. Pu, F. Li, X. Ding and V. S. Sheng, "Few-shot object detection based on the transformer and high-resolution network," Computers, Materials & Continua, vol. 74, no.2, pp. 3439–3454, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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