Hongje Seong
Papers
2
Total Citations
41
H-Index
2
About
Hongje Seong is a researcher whose work sits at the intersection of computer vision and robotics, with a focus on enabling intelligent machines to understand and navigate their environments. His key contributions lie in semantic segmentation and indoor scene understanding, both critical for autonomous systems. In his highly cited work on the **Adjacent Feature Propagation Network (AFPNet)** (2021, 20 citations), Seong addressed the pressing need for real-time semantic segmentation in mobile robots and autonomous vehicles, proposing a novel architecture that balances accuracy with computational efficiency. Complementing this, his research on **indoor place category recognition for cleaning robots** (2021, 21 citations) fuses probabilistic methods with deep learning to allow robots to predict room categories from onboard camera images—a task bridging scene recognition in computer vision and semantic mapping in robotics. With over 40 citations across his most prominent papers, Seong’s work demonstrates tangible impact in making visual perception both faster and more context-aware for real-world robotic applications. His contributions are particularly valuable for students and engineers developing autonomous systems that must operate reliably in complex, unstructured indoor spaces.
Research Focus
Key Achievements
Top Papers
- 1
- 2