Jayant Teotia
Papers
1
Total Citations
5
H-Index
1
About
Jayant Teotia is a researcher at the forefront of robotic perception and computer vision, specializing in multi-spectral fusion and knowledge distillation for safety-critical autonomous systems. His work addresses the fundamental challenge of maintaining reliable semantic segmentation in low-light and adverse environmental conditions, where traditional electro-optical sensors fail. Teotia’s most notable contribution, the SKD-Net framework, introduces a spectral-based knowledge distillation technique that enables aerial perception systems to generalize effectively across thermal and visible spectra. This approach enhances the robustness of robotic perception in challenging scenarios, such as nighttime or foggy environments, by leveraging complementary information from infrared imagery. With his 2024 paper already garnering 5 citations, Teotia’s research is gaining traction for its practical implications in autonomous navigation and surveillance. His work bridges the gap between theoretical deep learning and real-world deployment, offering a scalable solution for improving the safety and reliability of unmanned aerial vehicles and other robotic platforms. Teotia’s innovative fusion of spectral analysis with knowledge distillation positions him as an emerging leader in advancing perception systems for the next generation of autonomous technologies.
Research Focus
Key Achievements
Top Papers
- 1