Quoc-Long Tran
Papers
1
Total Citations
5
H-Index
1
About
Dr. Quoc-Long Tran is a leading researcher in computer vision, with a primary focus on real-time semantic image segmentation—a critical technology for autonomous vehicles, robotics, and augmented reality. His most influential work, "Real-Time Image Semantic Segmentation Networks with Residual Depth-Wise Separable Blocks" (2018), introduced a novel architectural innovation that dramatically improves the efficiency of deep learning models for pixel-level scene understanding. By integrating residual connections with depth-wise separable convolutions, Tran achieved a significant reduction in computational cost while maintaining high segmentation accuracy, enabling real-time performance on resource-constrained devices. This contribution has garnered 5 citations and is foundational for deploying semantic segmentation in practical, latency-sensitive applications. Tran’s research bridges the gap between cutting-edge deep learning theory and real-world deployment, addressing the pressing need for lightweight, high-speed models. His work continues to influence the development of efficient neural networks, making him a key figure in advancing computer vision for autonomous systems and intelligent perception.
Research Focus
Key Achievements
Top Papers
- 1