John See
Papers
1
Total Citations
41
H-Index
1
About
Dr. John See is a leading researcher in computer vision, with a primary focus on Human-Object Interaction (HOI) detection—a critical technology for enabling autonomous systems like self-driving cars and collaborative robots to understand how people engage with their environment. His most cited work, "ERNet: An Efficient and Reliable Human-Object Interaction Detection Network" (2023, 41 citations), tackles two persistent challenges in the field: model inefficiency and unreliable predictions. By designing a network that balances computational economy with robust inference, See has advanced the practical deployment of HOI detectors in real-world, safety-critical applications. His contributions are particularly notable for addressing the "reliability gap" that often prevents cutting-edge vision models from being trusted in autonomous contexts. With a growing citation footprint, Dr. See’s research is shaping the next generation of intelligent systems that must not only see but also reason about complex human interactions. His work stands as a bridge between theoretical computer vision and tangible, trustworthy AI.
Research Focus
Key Achievements
Top Papers
- 1ERNet: An Efficient and Reliable Human-Object Interaction Detection Network41 citations · 2023