Rethinking Patch Dependence for Masked Autoencoders
Letian Fu Long Lian Renhao Wang Baifeng Shi Xudong Wang Adam Yala Trevor Darrell Alexei A. Efros Ken Goldberg
In this work, we re-examine inter-patch dependencies in the decoding
mechanism of masked autoencoders (MAE). We decompose this decoding mechanism
for masked patch reconstruction in MAE into self-attention and cross-attention.
Our investigations suggest that self-attention between mask patches is not
essential for learning good representations. To this end, we propose a novel
pretraining framework: Cross-Attention Masked Autoencoders (CrossMAE).
CrossMAE's decoder leverages only cross-attention between masked and visible
tokens, with no degradation in downstream performance. This design also enables
decoding only a small subset of mask tokens, boosting efficiency. Furthermore,
each decoder block can now leverage different encoder features, resulting in
improved representation learning. CrossMAE matches MAE in performance with 2.5
to 3.7$\times$ less decoding compute. It also surpasses MAE on ImageNet
classification and COCO instance segmentation under the same compute. Code and
models: https://crossmae.github.io