HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking-Reference-Cited by-同舟云学术

HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking

Published:2022-10-23 Issue: Volume: Page:
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Container-title:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Fu Changhong¹,Dong Haolin¹,Ye Junjie¹,Zheng Guangze¹,Li Sihang¹,Zhao Jilin¹

Affiliation:

1. School of Mechanical Engineering, Tongji University,Shanghai,China,201804

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981070.pdf?arnumber=9981070

Reference33 articles.

1. Attention Is All You Need;vaswani;Proceedings of the Advances in Neural Information Processing Systems (NIPS),2017

2. Zero-Reference Deep Curve Estimation for Low-Light Image En-hancement;guo;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),2020

3. Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label

4. ECO: Efficient Convolution Operators for Tracking

5. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

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