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Despite their irresistible success, deep learning algorithms still heavilyrely on annotated data. On the other hand, unsupervised settings pose manychallenges, especially about determining the right inductive bias in diversescenarios. One scalable solution is to make the model generate the supervisionfor itself by leveraging some part of the input data, which is known asself-supervised learning. In this paper, we use the prediction task asself-supervision and build a novel object-centric model for image sequencerepresentation. In addition to disentangling the notion of objects and themotion dynamics, our compositional structure explicitly handles occlusion andinpaints inferred objects and background for the composition of the predictedframe. With the aid of auxiliary loss functions that promote spatially andtemporally consistent object representations, our self-supervised framework canbe trained without the help of any manual annotation or pretrained network.Initial experiments confirm that the proposed pipeline is a promising steptowards object-centric video prediction.

Self-Supervision by Prediction for Object Discovery in Videos