Rajan M.A.

Papers

Total Citations

H-Index

About

Dr. Rajan M.A. is a computer vision researcher whose work focuses on advancing scene understanding through graph-based representations. His key research area centers on scene graph classification, a challenging task that captures the rich semantic relationships between objects in an image. His most-cited paper, "EdgeNet for efficient scene graph classification" (2022), introduces a novel architecture that improves how machines interpret visual scenes by modeling objects and their interactions as nodes and edges of a graph. This work has direct implications for downstream applications like image retrieval and action recognition, where understanding context and relationships is critical. Though early in its trajectory, this contribution has already garnered attention in the field, demonstrating the potential of efficient graph-based methods for semantic visual reasoning. Dr. Rajan’s research bridges the gap between low-level visual features and high-level semantic understanding, offering a pathway toward more intelligent and context-aware AI systems. His work is particularly relevant for students and researchers interested in the intersection of graph neural networks and computer vision.