SCALABLE OBJECT-ORIENTED SEQUENTIAL GENERATIVE MODELS

Anonymous

Sep 25, 2019 Blind Submission readers: everyone Show Bibtex
  • Abstract: The most significant limitation of previous approaches to unsupervised learning for object-oriented representation is its scalability. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a generative model for Scalable Sequential Object-Oriented Representation. With the spatially parallel attention and proposal-rejection mechanism, SCALOR is a scalable model that can deal with orders of magnitude more objects that previous models. Besides, we introduce the background model so that it can model the foreground objects and complex background together. In experiments on large-scale MNIST and DSprite datasets, we demonstrate that SCALOR can deal with scenes with near 100 objects as well as modeling complex natural background images. Importantly, using SCALOR, we demonstrate for the first time a result of modeling natural scenes with several tens of moving objects
0 Replies

Loading