  Masked Image Modeling (MIM) has achieved promising progress with the advent
of Masked Autoencoders (MAE) and BEiT. However, subsequent works have
complicated the framework with new auxiliary tasks or extra pre-trained models,
inevitably increasing computational overhead. This paper undertakes a
fundamental analysis of MIM from the perspective of pixel reconstruction, which
examines the input image patches and reconstruction target, and highlights two
critical but previously overlooked bottlenecks. Based on this analysis, we
propose a remarkably simple and effective method, {\ourmethod}, that entails
two strategies: 1) filtering the high-frequency components from the
reconstruction target to de-emphasize the network's focus on texture-rich
details and 2) adopting a conservative data transform strategy to alleviate the
problem of missing foreground in MIM training. {\ourmethod} can be easily
integrated into most existing pixel-based MIM approaches (\ie, using raw images
as reconstruction target) with negligible additional computation. Without bells
and whistles, our method consistently improves three MIM approaches, MAE,
ConvMAE, and LSMAE, across various downstream tasks. We believe this effective
plug-and-play method will serve as a strong baseline for self-supervised
learning and provide insights for future improvements of the MIM framework.
Code and models are available at
\url{https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim}.
