Compositional Scene Representation Learning via Reconstruction: A Survey

TL;DR

This survey reviews recent advances in learning compositional scene representations through reconstruction using deep neural networks, highlighting progress, benchmarks, limitations, and future directions in the field.

Contribution

It provides a comprehensive overview of reconstruction-based compositional scene representation learning methods, including development history, categorizations, benchmarks, and open source tools.

Findings

Progress in deep learning methods for scene representation

Benchmark datasets and open source toolbox provided

Discussion on limitations and future research directions

Abstract

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.