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title
CrossMAE: Rethinking Patch Dependence for Masked Autoencoders
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CrossMAE: Rethinking Patch Dependence for Masked Autoencoders
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author
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2026-01-18 21:15:06
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CrossMAE: Rethinking Patch Dependence for Masked Autoencoders CrossMAE Rethinking Patch Dependence for Masked Autoencoders Letian Fu 1* Long Lian 1* Renhao Wang 1 Baifeng Shi 1 Xudong Wang 1 Adam Yala 1,2† Trevor Darrell 1† Alexei A. Efros 1† Ken Goldberg 1† 1 UC Berkeley 2 UCSF * Equal Contribution † Equal Advising Paper Code Citation TL;DR : Learning visual representation doesn't require the model to generate self-consistent images. Overview We introduce Cross-Attention Masked Autoencoders (CrossMAE) , which use only cross-attention for decoding in MAE. We show that CrossMAE greatly enhances efficiency and performance in tasks like ImageNet classification and COCO instance segmentation, with significantly reduced computational ...
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