Masked Modeling for Self-supervised Representation Learning on Vision and Beyond

TL;DR

This paper provides a comprehensive review of masked modeling techniques in self-supervised learning, highlighting its methodologies, applications across domains, and future research directions.

Contribution

It systematically analyzes masked modeling frameworks, compares methods across fields, and discusses limitations and future prospects in self-supervised representation learning.

Findings

Masked modeling enhances robust representation learning.

It is effective across vision, language, and other modalities.

The survey identifies key challenges and future research directions.

Abstract

As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data. Among these varied self-supervised techniques, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training. This paradigm enables deep models to learn robust representations and has demonstrated exceptional performance in the context of computer vision, natural language processing, and other modalities. In this survey, we present a comprehensive review of the masked modeling framework and its methodology. We elaborate on the details of techniques within masked modeling, including diverse masking strategies, recovering targets, network architectures, and more. Then, we systematically investigate its wide-ranging applications across domains. Furthermore, we also explore the commonalities and differences between masked modeling methods in different fields. Toward the end of this paper, we conclude by discussing the limitations of current techniques and point out several potential avenues for advancing masked modeling research. A paper list project with this survey is available at \url{https://github.com/Lupin1998/Awesome-MIM}.