#Masked Image Modeling

This project delivers an extensive review of Masked Image Modeling (MIM) and associated techniques in self-supervised representation learning, presenting them in their historical sequence of development. It covers essential topics such as MIM for Transformers, contrastive learning, and applications in various modalities. The analysis includes the progression of self-supervised learning across diverse modalities, underscoring its pivotal role since 2018 in areas like NLP and Computer Vision. Contributions and revisions from the community are welcomed, along with resources such as curated paper lists and formats for academic citations. This is an essential resource for researchers and enthusiasts exploring the developments and practical applications in MIM.

Utilizing Masked Image Modeling with a Vanilla ViT, this project enhances object detection and instance segmentation. A compact convolutional stem is integrated for multi-scale representation, forming a hybrid ViT-ConvNet backbone. It achieves significant results on COCO with 51.7 box AP and 46.2 mask AP, showcasing efficiency in training and accuracy in inference through varied sample ratios.