A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance

Author:

Chu Yinghao1ORCID,Feng Daquan2ORCID,Liu Zuozhu3ORCID,Zhang Lei4ORCID,Zhao Zizhou5,Wang Zhenzhong6ORCID,Feng Zhiyong7ORCID,Xia Xiang-Gen8ORCID

Affiliation:

1. Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

2. Shenzhen Key Laboratory of Digital Creative Technology, the Guangdong Province Engineering Laboratory for Digital Creative Technology, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

3. ZJU-UIUC Institute, Zhejiang University, Hangzhou, China

4. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

5. AIATOR Company Ltd., Shenzhen, China

6. Technical Management Center, China Media Group, Beijing, China

7. Key Laboratory of the Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China

8. Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA

Funder

National Science and Technology Major Project

Shenzhen Science and Technology Program

Open Foundation of State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Reference102 articles.

1. R-FCN: Object detection via region-based fully convolutional networks;dai;Proc Adv Neural Inf Process Syst,2016

2. Faster R-CNN: Towards real-time object detection with region proposal networks;ren;Proc Adv Neural Inf Process Syst,2015

3. Scalable Object Detection Using Deep Neural Networks

4. Feature Pyramid Networks for Object Detection

5. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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