Object Centric

Object-centric approaches in computer vision aim to represent scenes as collections of individual objects and their relationships, rather than processing images holistically. Current research focuses on developing models that can robustly identify and track objects across time and viewpoints, often employing transformer networks, slot attention mechanisms, and graph neural networks to achieve this. This shift towards object-centric representations promises improved generalization, interpretability, and efficiency in various applications, including robotic manipulation, video understanding, and anomaly detection.