Scalable Object-Oriented Sequential Generative Models

In SCALOR, we achieve scalability with respect to the object density by parallelizing both the propagation and discovery processes, reducing the parallel time complexity per scene image to O (1) from O (N) with N the number of objects in an image. We also observe that the serial object processing in SQAIR based on an RNN not only increases the computation time but also deteriorates discovery performance. To this end, we propose a parallel discovery model with much better discovery capacity and performance. Temporally predicting and detecting trajectories of objects, SCALOR can also be regarded as a generative tracking model. In our experiments, we show that SCALOR can model videos with nearly one hundred moving objects along with complex background on synthetic datasets. Furthermore, we evaluate and demonstrate SCALOR on natural videos as well with tens of objects with complex background. The contribution of this work are: (i) We propose the SCALOR model that significantly improves (two orders of magnitude) the scalability with regard to the the object density. It is applicable to nearly a hundred objects with comparable computation time to SQAIR, which scales only to a few objects.