Shyam Sundar Kannan

Papers

Total Citations

H-Index

About

Shyam Sundar Kannan is an emerging researcher working at the intersection of computer vision and robotics, with a focused interest in scene understanding and change detection. His notable work includes "ZeroSCD: Zero-Shot Street Scene Change Detection" (2025), which addresses one of the more nuanced challenges in autonomous systems — identifying meaningful differences between images of the same scene captured at different points in time. What sets this contribution apart is its zero-shot approach, moving beyond the conventional paradigm of training models explicitly on paired image inputs to detect changes. This represents a significant methodological shift, reducing dependency on labeled training data and broadening the practical applicability of change detection systems in real-world robotics and autonomous driving contexts. While still early in accumulating citations, the work has already begun attracting attention within the community, reflecting its relevance to pressing problems in robotic perception and scene monitoring. Kannan's research sits at a timely crossroads of generalization in deep learning and embodied AI, positioning him as a promising voice in next-generation computer vision research focused on robust, data-efficient scene analysis.