  Visual scenes are composed of visual concepts and have the property of
combinatorial explosion. An important reason for humans to efficiently learn
from diverse visual scenes is the ability of compositional perception, and it
is desirable for artificial intelligence to have similar abilities.
Compositional scene representation learning is a task that enables such
abilities. In recent years, various methods have been proposed to apply deep
neural networks, which have been proven to be advantageous in representation
learning, to learn compositional scene representations via reconstruction,
advancing this research direction into the deep learning era. Learning via
reconstruction is advantageous because it may utilize massive unlabeled data
and avoid costly and laborious data annotation. In this survey, we first
outline the current progress on reconstruction-based compositional scene
representation learning with deep neural networks, including development
history and categorizations of existing methods from the perspectives of the
modeling of visual scenes and the inference of scene representations; then
provide benchmarks, including an open source toolbox to reproduce the benchmark
experiments, of representative methods that consider the most extensively
studied problem setting and form the foundation for other methods; and finally
discuss the limitations of existing methods and future directions of this
research topic.
