Xiangbo Shu - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Xiangbo Shu

Also published under: xiangbo shu

Publications

39

Citations

2,064

Publications by Year

20152025

Co-Authors:

Keke ChenZhen CuiQiongjie CuiGuangzhao DaiGuodong Du

Show All Co-Authors (75)

Xiangbo Shu

Also published under: xiangbo shu

Affiliation

School of Computer Science and Engineering

Nanjing University of Science and Technology

Nanjing, China

Publication Topics

Action Recognition,
Global Features,
Graph Convolutional Network,
Training Set,
Action Classes,
Bounding Box,
Human Activity Recognition,
Self-supervised Learning,
Visual Features,
Action Representation,
Appearance Features,
Contrastive Loss

Biography

Xiangbo Shu is currently working toward the PhD degree in the School of Computer Science and Engineering, Nanjing University of Science and Technology, China. From 2014 to 2015, he was also an visiting scholar in the Department of Electrical and Computer Engineering, National University of Singapore. His research interests include social multimedia computing and computer vision. He received the Best Student Paper Award in MMM 2016 and the Best Paper Finalist in ACM MM 2015.(Based on document published on 13 September 2016).

Publications

39

Citations

2,064

Publications by Year

20152025

Co-Authors:

Keke Chen
Zhen Cui
Qiongjie Cui
Guangzhao Dai
Guodong Du

Show All Co-Authors (75)

Author's Published Works

Search History

Showing 1-4 of 4 results

Journals (3)

Conferences (1)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
Fan Yang(4)
Wenrui Chen(3)
Zhiyong Li(3)
Xuan He(2)
Xin Li(2)
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, China(3)
School of Robotics, Hunan University, Changsha, China(3)
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China(2)
School of Computer Science, Hefei University of Technology, Hefei, China(1)
National Engineering Research Center for Robot Visual Perception and Control, College of Electrical and Information Engineering, Hunan University, Changsha, China(1)
2023 China Automation Congress (CAC)(1)
IEEE Transactions on Cybernetics(1)
IEEE Transactions on Instrumentation and Measurement(1)
IEEE Transactions on Intelligent Vehicles(1)
IEEE(4)
Chongqing, China(1)
Attention Layer(2)
Attention Mechanism(2)
Attention Module(2)
Depth Features(2)
Dexterous Hand(2)

Select All on Page

Sort By

Results

Task-Oriented Tool Manipulation With Robotic Dexterous Hands: A Knowledge Graph Approach From Fingers to Functionality

Fan Yang;Wenrui Chen;Haoran Lin;Sijie Wu;Xin Li;Zhiyong Li;Yaonan Wang

IEEE Transactions on Cybernetics

Year: 2025 | Volume: 55, Issue: 1 | Journal Article |

HTML

A primary challenge in robotic tool use is achieving precise manipulation with dexterous robotic hands to mimic human actions. It requires understanding human tool use and allocating specific functions to each robotic finger for fine control. Existing work has primarily focused on the overall grasping capabilities of robotic hands, often neglecting the functional allocation among individual finger...Show More

Task-Oriented Tool Manipulation With Robotic Dexterous Hands: A Knowledge Graph Approach From Fingers to Functionality

Fan Yang;Wenrui Chen;Haoran Lin;Sijie Wu;Xin Li;Zhiyong Li;Yaonan Wang

IEEE Transactions on Cybernetics

Year: 2025 | Volume: 55, Issue: 1 | Journal Article |

MonoPSTR: Monocular 3-D Object Detection With Dynamic Position and Scale-Aware Transformer

Fan Yang;Xuan He;Wenrui Chen;Pengjie Zhou;Zhiyong Li

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

HTML

Transformer-based approaches have demonstrated outstanding performance in monocular 3-D object detection, which involves predicting 3-D attributes from a single 2-D image. These transformer-based methods typically rely on visual and depth representations to identify crucial queries related to objects. However, the feature and location of queries are expected to learn adaptively without any prior k...Show More

MonoPSTR: Monocular 3-D Object Detection With Dynamic Position and Scale-Aware Transformer

Fan Yang;Xuan He;Wenrui Chen;Pengjie Zhou;Zhiyong Li

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

SSD-MonoDETR: Supervised Scale-Aware Deformable Transformer for Monocular 3D Object Detection

Xuan He;Fan Yang;Kailun Yang;Jiacheng Lin;Haolong Fu;Meng Wang;Jin Yuan;Zhiyong Li

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (7)

HTML

Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods leverage both visual and depth representations to explore valuable query points on objects, and the quality of the learned query points has a great impact on detection accuracy. Unfortunat...Show More

SSD-MonoDETR: Supervised Scale-Aware Deformable Transformer for Monocular 3D Object Detection

Xuan He;Fan Yang;Kailun Yang;Jiacheng Lin;Haolong Fu;Meng Wang;Jin Yuan;Zhiyong Li

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Within-Hand Manipulation with an Underactuated Dexterous Hand Based on Pre-Trained Reinforcement Learning

Xin Li;Wenrui Chen;Yaonan Wang;Qiang Diao;Sijie Wu;Fan Yang

2023 China Automation Congress (CAC)

Year: 2023 | Conference Paper |

HTML

This paper investigates a dexterous manipulation method for multi-fingered hands based on the approximate policy optimization (PPO) algorithm. First, a prior knowledge base of dexterous manipulation demonstration data is provided for the PPO algorithm, and the original model is pre-trained to guide fast network training. Second, a new reward function is proposed to optimize rotational manipulation...Show More

Within-Hand Manipulation with an Underactuated Dexterous Hand Based on Pre-Trained Reinforcement Learning

Xin Li;Wenrui Chen;Yaonan Wang;Qiang Diao;Sijie Wu;Fan Yang

2023 China Automation Congress (CAC)

Year: 2023 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.