Object Centric Representation

Object-centric representation aims to model scenes as compositions of individual objects and their relationships, enabling more robust and generalizable AI systems. Current research focuses on developing unsupervised learning methods, often employing transformer networks, slot attention mechanisms, and generative models (like NeRFs) to learn these representations from various data modalities (images, videos, point clouds). This approach promises significant improvements in tasks requiring compositional understanding, such as robotics, visual question answering, and scene prediction, by moving beyond pixel-level processing to a more human-like understanding of the world. The resulting disentangled representations also enhance interpretability and facilitate zero-shot generalization across diverse domains.