Jing-Jhih Lin
Papers
2
Total Citations
255
H-Index
2
About
Jing-Jhih Lin is a computer vision researcher whose work centers on the intersection of deep learning efficiency and semantic scene understanding. Best known for pioneering contributions to real-time semantic segmentation, Lin has addressed one of the field's most pressing challenges: achieving high accuracy without sacrificing computational speed. Their landmark paper, "Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation," has accumulated over 228 citations, underscoring its significant influence on the research community. In this work, Lin introduced dense modules built upon asymmetric convolution operations — an innovative architectural design that enables faster inference while maintaining competitive segmentation performance, making it particularly valuable for time-sensitive applications such as autonomous driving and robotics. At a time when most segmentation research prioritized accuracy alone, Lin's approach offered a compelling and practical alternative that bridged the gap between theoretical performance and real-world deployment constraints. This focus on efficiency-driven design reflects a broader commitment to making advanced computer vision techniques accessible and applicable in embedded and edge computing environments. Lin's contributions continue to inspire researchers seeking to balance speed and precision in perception systems.
Research Focus
Key Achievements
Top Papers
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