Huiyong Li
Papers
1
Total Citations
7
H-Index
1
About
Huiyong Li is a leading researcher in computer vision and robotics, with a primary focus on affordance detection—a critical capability that enables robots to understand how to interact with objects in their environment. His most-cited work, "Multi-scale Fusion and Global Semantic Encoding for Affordance Detection" (2022, 7 citations), addresses a fundamental challenge in robotics: the need for fast, accurate detection of object affordances. Li’s major contribution lies in developing a single-stage affordance detector that overcomes the speed limitations of traditional two-stage object detection methods, allowing robots to rapidly identify interaction possibilities. By integrating multi-scale feature fusion with global semantic encoding, his approach significantly improves both detection efficiency and accuracy. This work has direct implications for advancing autonomous robotic manipulation, where real-time understanding of object functionality is essential. Li’s research continues to push the boundaries of how machines perceive and interact with the physical world, making him a notable figure in the intersection of computer vision and robotics.
Research Focus
Key Achievements
Top Papers
- 1Multi-scale Fusion and Global Semantic Encoding for Affordance Detection7 citations · 2022