Lei Yuan
Papers
1
Total Citations
12
H-Index
1
About
Lei Yuan is an emerging researcher whose work sits at the intersection of computer vision and robotic perception, with a particular focus on **visual affordance detection** — the challenge of enabling machines to understand how objects can be used or interacted with in the physical world. Yuan's most recognized contribution, the 2021 paper *"Visual Affordance Detection Using an Efficient Attention Convolutional Neural Network,"* demonstrates a sophisticated integration of attention mechanisms within convolutional neural network architectures to improve the speed and accuracy of affordance detection. This work, which has garnered 12 citations since its publication, reflects a growing community interest in making robotic and autonomous systems more contextually aware of their environments. By leveraging attention-based approaches, Yuan's research addresses a critical bottleneck in human-robot interaction and scene understanding — helping machines not just identify objects, but reason about their functional properties. Yuan's contributions represent meaningful progress in a field with broad implications for robotics, augmented reality, and autonomous navigation, positioning them as a promising voice in the next generation of computer vision research.
Research Focus
Key Achievements
Top Papers
- 1