Min Shi
Papers
1
Total Citations
104
H-Index
1
About
Min Shi is a leading researcher in efficient deep learning for computer vision, with a focus on real-time semantic segmentation for autonomous driving and robotics. His most impactful work, "LMFFNet: A Well-Balanced Lightweight Network for Fast and Accurate Semantic Segmentation" (2022, 104 citations), addresses a critical challenge in the field: the trade-off between model complexity and segmentation accuracy. While prior state-of-the-art networks achieved high accuracy through computationally heavy architectures, and lightweight models often sacrificed performance for speed, Shi’s LMFFNet introduced a carefully balanced design that maintains strong segmentation accuracy while drastically reducing parameter size and computational cost. This breakthrough makes it highly suitable for resource-constrained, real-time applications. Beyond this flagship paper, Shi’s research consistently advances the frontier of efficient, deployable vision models, demonstrating how principled architectural design can bridge the gap between academic performance benchmarks and practical deployment in autonomous systems. His work is widely cited by researchers and engineers developing next-generation perception systems for self-driving cars and intelligent robots.
Research Focus
Key Achievements
Top Papers
- 1