Papers
19
Total Citations
1,538
H-Index
17
About
Dr. Wenfeng Zheng is a leading researcher at the intersection of artificial intelligence, medical robotics, and control systems. His work primarily focuses on visual question answering (VQA), deep reinforcement learning, and advanced teleoperation systems. Zheng’s most impactful contribution is his 2023 paper on "Multiscale Feature Extraction and Fusion of Image and Text in VQA," which has garnered 414 citations and addresses how AI can intelligently interpret visual data to answer questions—a breakthrough for applications in visual assistance and automated surveillance. He is also known for pioneering the use of Twin-Delayed DDPG (TD3) for robotic locomotion, earning 155 citations, and for developing innovative soft tissue modeling and tracking methods for medical robots (151 and 97 citations, respectively). His work on adaptive control for time-delay teleoperation systems (141 citations) and depth estimation for monocular cameras in microscopic scenes (70 citations) further showcases his versatility. With over 1,300 total citations across his top papers, Zheng’s research is shaping the future of intelligent robotics and human-machine interaction, making him a key figure in both theoretical and applied AI.
Research Focus
Key Achievements
Top Papers
- 1Multiscale Feature Extraction and Fusion of Image and Text in VQA414 citations · 2023
- 2Twin-Delayed DDPG155 citations · 2019
- 3An improved method for soft tissue modeling151 citations · 2020
- 4Adaptive control of time delay teleoperation system with uncertain dynamics141 citations · 2022
- 5Soft Tissue Feature Tracking Based on Deep Matching Network97 citations · 2023
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- 9A Novel Architecture of a Six Degrees of Freedom Parallel Platform62 citations · 2023
- 10