Jiayang Cheng
Papers
1
Total Citations
2
H-Index
1
About
Dr. Jiayang Cheng is a rising researcher in computer vision, with a primary focus on efficient deep learning architectures for real-time semantic segmentation. Their most notable contribution is the development of the "Global-feature Enhanced Network for Fast Semantic Segmentation" (2023), an innovative dual-branch network that addresses a critical bottleneck in lightweight models: the trade-off between speed and semantic understanding. By enhancing the deep network branch to better capture global contextual information, Dr. Cheng’s work enables fast, high-quality segmentation without sacrificing real-time performance—a key requirement for autonomous driving and robotics. Though early in their career, this work has already garnered 2 citations, signaling growing interest from the community. Dr. Cheng’s research is particularly valuable for students and engineers seeking to deploy efficient vision systems on resource-constrained devices, as it demonstrates how to balance computational efficiency with the rich feature extraction needed for accurate scene parsing. Their ongoing work promises to further bridge the gap between speed and accuracy in real-world AI applications.
Research Focus
Key Achievements
Top Papers
- 1Global-feature Enhanced Network for Fast Semantic Segmentation2 citations · 2023