Object Representation

Object representation in artificial intelligence focuses on developing computational models that can effectively encode and utilize information about objects within scenes, mirroring human-like object understanding. Current research emphasizes learning compositional and disentangled representations using architectures like transformers, graph neural networks, and variational autoencoders, often incorporating self-supervised or contrastive learning methods to improve generalization. These advancements are crucial for improving performance in various applications, including robotic manipulation, visual reasoning, and scene understanding, by enabling more robust and flexible interaction with the visual world.