Summary of Masked Modeling For Self-supervised Representation Learning on Vision and Beyond, by Siyuan Li et al.
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
by Siyuan Li, Luyuan Zhang, Zedong Wang, Di Wu, Lirong Wu, Zicheng Liu, Jun Xia, Cheng Tan, Yang Liu, Baigui Sun, Stan Z. Li
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The masked modeling framework in self-supervised learning has shown remarkable representation learning ability and low dependence on labeled data, making it a promising approach for computer vision, natural language processing, and other modalities. This survey provides a comprehensive review of the methodology, including techniques such as diverse masking strategies, recovering targets, network architectures, and more. The applications across domains are investigated systematically, highlighting commonalities and differences between masked modeling methods in different fields. While current techniques have limitations, this research has potential avenues for advancing masked modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Masked modeling is a type of self-supervised learning that predicts parts of the original data that are proportionally masked during training. This approach enables deep models to learn robust representations and has shown great performance in various domains. In this survey, we explore the details of masked modeling techniques, including different masking strategies, recovering targets, network architectures, and more. We also discuss its wide-ranging applications across computer vision, natural language processing, and other modalities. |
Keywords
» Artificial intelligence » Natural language processing » Representation learning » Self supervised
