  We present a slot-wise, object-based transition model that decomposes a scene
into objects, aligns them (with respect to a slot-wise object memory) to
maintain a consistent order across time, and predicts how those objects evolve
over successive frames. The model is trained end-to-end without supervision
using losses at the level of the object-structured representation rather than
pixels. Thanks to its alignment module, the model deals properly with two
issues that are not handled satisfactorily by other transition models, namely
object persistence and object identity. We show that the combination of an
object-level loss and correct object alignment over time enables the model to
outperform a state-of-the-art baseline, and allows it to deal well with object
occlusion and re-appearance in partially observable environments.

Computer Vision and Pattern Recognition;Artificial Intelligence