Object Recognition Model

Object recognition models aim to enable computers to identify and classify objects within images or videos, mirroring human visual perception. Current research focuses on improving model robustness against adversarial attacks and variations in geographical context, often leveraging techniques like centroid triplet loss for efficient training and incorporating language descriptions or knowledge graphs to enhance contextual understanding. These advancements are crucial for applications ranging from autonomous vehicles and robotics to digital heritage preservation, driving progress in both computer vision and artificial intelligence.