Summary of Masked Image Modeling: a Survey, by Vlad Hondru et al.
Masked Image Modeling: A Survey
by Vlad Hondru, Florinel Alin Croitoru, Shervin Minaee, Radu Tudor Ionescu, Nicu Sebe
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper surveys recent studies on masked image modeling (MIM), a self-supervised learning technique in computer vision. MIM involves masking information, such as pixels or latent representations, and training an autoencoder to predict the missing data based on visible context. The authors identify two categories of approaches: reconstruction-based and contrastive learning-based. They construct a taxonomy, review prominent papers, and compare performance results on popular datasets. The paper also highlights research gaps and proposes future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers learn to recognize pictures without being shown the whole image. It’s like trying to complete a puzzle by looking only at some of the pieces. The authors looked at different ways that computers do this, and how well they work compared to each other. They also found out what kinds of images are best for this kind of learning. Finally, they said what areas need more research. |
Keywords
» Artificial intelligence » Autoencoder » Self supervised
