Yueqiu Jiang
Papers
5
Total Citations
223
H-Index
4
About
Yueqiu Jiang is a leading researcher at the intersection of computer vision, robotics, and multi-agent systems, with a career defined by solving fundamental challenges in autonomous perception and intelligent control. Her work spans three core areas: robust visual feature matching for robot navigation, advanced human action recognition, and multi-agent reinforcement learning. Her most influential contribution is a 2017 study on zooming-image false match elimination for robot navigation, which has garnered 137 citations and provides critical algorithms for scale-invariant feature matching in dynamic environments. She further advanced the field with a 2020 discriminative deep model that integrates feature fusion and temporal attention to improve human action recognition accuracy in complex, long-duration scenarios (42 citations). Demonstrating her breadth, Jiang authored a comprehensive 2025 review of multi-agent reinforcement learning algorithms (35 citations), synthesizing progress in robotic collaboration and game AI. Her more recent work includes a novel large-kernel encoder-decoder network for maritime image dehazing (2022) and a monocular vision localization method for precision manipulator control (2023). Through these contributions, Jiang has established herself as a versatile innovator, bridging theoretical advances in machine learning with practical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1Zooming image based false matches elimination algorithms for robot navigation137 citations · 2017
- 2
- 3A Review of Multi-Agent Reinforcement Learning Algorithms35 citations · 2025
- 4
- 5