Vision-specific concepts such as "region" have played a key role in extending
general machine learning frameworks to tasks like object detection. Given the
success of region-based detectors for supervised learning and the progress of
intra-image methods for contrastive learning, we exp
Multi-level Optimized Mask Autoencoder (MLO-MAE) is a novel framework for visual representation learning that leverages end-to-end feedback from downstream tasks to learn an optimal masking strategy during pretraining, demonstrating remarkable improvements in adaptability and efficiency compared to existing methods.