Ge Su
Papers
1
Total Citations
49
H-Index
1
About
Ge Su is a leading researcher in computer vision and intelligent systems, with a primary focus on bioinspired scene classification and deep active learning. Her most impactful work, "Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications" (2021, 49 citations), introduces a novel framework that emulates biological visual perception to accurately classify sceneries with varying spatial configurations—a critical capability for scene parsing, robot motion planning, and autonomous driving. By integrating deep active learning, Su’s approach reduces the need for extensive labeled data while enhancing model robustness, significantly advancing remote sensing applications. Her contributions bridge the gap between biological inspiration and practical AI, demonstrating how adaptive learning strategies can improve real-world performance in complex environments. With a growing citation record, Su’s research is recognized for its innovation in tackling spatial complexity, making her a notable figure in the development of more efficient and intelligent vision systems. Her work continues to influence both academic research and industrial applications in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1