All code can be downloaded from https://github.com/Shanka123/OCRA, Figure task is to S1: say Abstract whether Reasoning they are the T same asks (AR or dif T). ferent. Same/differ Relational ent: matc Two h-to-sample: objects are presented, A source and pair the of objects is presented that either instantiates a'same' or'different' relation, and the task is to select the pair in a 2 of tar 2 get array objects format, (out with of tw the o pairs) source th pair at instantiates presented in the the same top relation. The of task is to select the missing object from a set of four choices. Problems were presented in a 2 3 array each answer format, choice, with one see of Figure the answer S8). Identity choices rules: inserted An into abstract the bottom pattern right is instantiated cell (separate in the images first ro for w (AB instantiated A, ABB, in or the AAA), second and ro the w.

Co-first author order is arbitrary and may be swapped when citing this work.

Learning modular object-centric representations is crucial for systematic generalization.

From these temporally-aware slots, the training objective is to reconstruct the middle frame in a high-level semantic feature space. We propose a masking strategy by dropping a significant portion of tokens in the feature space for efficiency and regularization. Additionally, we address over-clustering by merging slots based on similarity.

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-levelperceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes.

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.