To enable explicit representation of objects in neural architectures, a core challenge lies in defining a mapping from input features (e.g., an image encoded by a CNN) to a set of abstract object representations. In this talk, I will discuss how attention mechanisms can be used in an iterative, competitive fashion to (a) efficiently group visual features into object slots and (b) segment temporal representations. I will further highlight how graph neural networks can be utilized to learn about interactions between objects and how object-centric models can be trained in a self-supervised fashion using contrastive losses.