Mask Based Modeling

Mask-based modeling is a rapidly developing technique used to improve various machine learning models, primarily by selectively masking parts of input data during training to encourage more robust and generalized learning. Current research focuses on applying this approach to diverse areas, including video generation (using diffusion models and transformers), neural radiance fields for 3D scene representation, and self-supervised learning for image classification and object detection. This methodology enhances model performance across various tasks by promoting better feature extraction, improved generalization across different datasets, and more efficient use of training data, leading to advancements in computer vision and other related fields.