Xianglin Li
Papers
1
Total Citations
17
H-Index
1
About
Xianglin Li’s research centers on advancing computer vision and machine learning, with a particular focus on efficient scene classification and interactive learning systems. His most-cited work, “Incorporating Incremental and Active Learning for Scene Classification” (2012, 17 citations), addresses a critical bottleneck in visual recognition: the high cost of manually labeling training data. Li proposed a novel framework that combines incremental learning with active learning, enabling classifiers to improve over time while strategically selecting only the most informative examples for human annotation. This approach significantly reduces labeling effort without sacrificing accuracy, making it highly relevant for applications like organizing personal photo libraries or guiding autonomous robots through complex environments. By tackling the practical challenge of data efficiency, Li’s contributions have influenced subsequent research in semi-supervised and interactive learning paradigms. His work exemplifies how thoughtful algorithm design can bridge the gap between theoretical machine learning and real-world deployment constraints.
Research Focus
Key Achievements
Top Papers
- 1Incorporating Incremental and Active Learning for Scene Classification17 citations · 2012