  Despite their irresistible success, deep learning algorithms still heavily
rely on annotated data. On the other hand, unsupervised settings pose many
challenges, especially about determining the right inductive bias in diverse
scenarios. One scalable solution is to make the model generate the supervision
for itself by leveraging some part of the input data, which is known as
self-supervised learning. In this paper, we use the prediction task as
self-supervision and build a novel object-centric model for image sequence
representation. In addition to disentangling the notion of objects and the
motion dynamics, our compositional structure explicitly handles occlusion and
inpaints inferred objects and background for the composition of the predicted
frame. With the aid of auxiliary loss functions that promote spatially and
temporally consistent object representations, our self-supervised framework can
be trained without the help of any manual annotation or pretrained network.
Initial experiments confirm that the proposed pipeline is a promising step
towards object-centric video prediction.
