  Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework,
offering remarkable performance across a wide range of downstream tasks. To
increase the difficulty of the pretext task and learn richer visual
representations, existing works have focused on replacing standard random
masking with more sophisticated strategies, such as adversarial-guided and
teacher-guided masking. However, these strategies depend on the input data thus
commonly increasing the model complexity and requiring additional calculations
to generate the mask patterns. This raises the question: Can we enhance MAE
performance beyond random masking without relying on input data or incurring
additional computational costs? In this work, we introduce a simple yet
effective data-independent method, termed ColorMAE, which generates different
binary mask patterns by filtering random noise. Drawing inspiration from color
noise in image processing, we explore four types of filters to yield mask
patterns with different spatial and semantic priors. ColorMAE requires no
additional learnable parameters or computational overhead in the network, yet
it significantly enhances the learned representations. We provide a
comprehensive empirical evaluation, demonstrating our strategy's superiority in
downstream tasks compared to random masking. Notably, we report an improvement
of 2.72 in mIoU in semantic segmentation tasks relative to baseline MAE
implementations.
