1. Introduction  
2. Background and Terminology  
3. Masked Auto Encoding: NLP to Vision  
3.1 NLP and vision followed different SSL paths  
3.2 Is generative SSL suitable for vision?  
3.3 Summary and remark  
4. Masked Auto Encoder for Image Modeling  
4.1 BEiT and its improved variants  
4.2 End-to-end masked auto encoder  
4.3 Towards improving efficiency  
4.4 Various perspectives on the success of masked auto encoder in vision  
4.5 Relationship with joint-embedding methods  
5. Other Applications: Vision and Beyond  
5.1 Vision related applications  
5.2 Beyond vision  
6. Conclusion