Saqib Mamoon

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Dr. Saqib Mamoon is a researcher at the forefront of efficient deep learning for computer vision, with a primary focus on real-time semantic segmentation. His most cited work, "SPSSNet: a real-time network for image semantic segmentation" (2020, 9 citations), tackles a critical bottleneck in deploying DNNs for practical applications: the trade-off between accuracy and computational speed. By addressing the excessive feature channels, parameters, and floating-point operations that make traditional networks sluggish, Dr. Mamoon’s contributions enable high-performance segmentation on resource-constrained devices, a vital step for autonomous driving, robotics, and mobile vision. His research directly impacts the development of lightweight architectures that maintain precision without sacrificing real-time performance. With a growing citation footprint, Dr. Mamoon’s work is recognized for its practical relevance, bridging the gap between theoretical advances and deployable AI systems. His achievements underscore a commitment to making deep learning more accessible and efficient, positioning him as a key voice in the evolution of real-time visual understanding.