Qingming Yi
Papers
1
Total Citations
104
H-Index
1
About
Qingming Yi is a leading researcher in efficient computer vision, specializing in 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 trade-off in the field: the tension between model complexity and real-world deployability. While prior networks achieved high accuracy through massive computational overhead, and lightweight alternatives often sacrificed precision for speed, Yi's LMFFNet introduced a carefully balanced architecture that maintains strong segmentation accuracy while dramatically reducing parameter sizes. This work has become a key reference for researchers seeking practical, deployable vision systems. Beyond this flagship contribution, Yi's broader research explores how to design neural networks that are both computationally efficient and accurate enough for safety-critical applications like autonomous navigation. His work bridges the gap between theoretical model design and real-time performance constraints, making him a notable figure in the push toward practical, edge-deployable AI.
Research Focus
Key Achievements
Top Papers
- 1