Slot Attention

An unsupervised object-centric representation learning mechanism proposed by Locatello et al. in 2020. It iteratively assigns image features to a fixed number of slot vectors via competitive attention, with each slot capturing information about a distinct object in the scene, requiring no object-level annotations during training.