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tags: |
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- mae |
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- crossmae |
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datasets: |
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- imagenet-1k |
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--- |
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## CrossMAE: Rethinking Patch Dependence for Masked Autoencoders |
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by <a href="https://max-fu.github.io">Letian Fu*</a>, <a href="https://tonylian.com">Long Lian*</a>, <a href="https://renwang435.github.io">Renhao Wang</a>, <a href="https://bfshi.github.io">Baifeng Shi</a>, <a href="https://people.eecs.berkeley.edu/~xdwang">Xudong Wang</a>, <a href="https://www.adamyala.org">Adam Yala†</a>, <a href="https://people.eecs.berkeley.edu/~trevor">Trevor Darrell†</a>, <a href="https://people.eecs.berkeley.edu/~efros">Alexei A. Efros†</a>, <a href="https://goldberg.berkeley.edu">Ken Goldberg†</a> at UC Berkeley and UCSF |
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[[Paper](https://arxiv.org/abs/2401.14391)] | [[Project Page](https://crossmae.github.io/)] | [[Citation](#citation)] |
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<p align="center"> |
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<img src="https://crossmae.github.io/crossmae2.jpg" width="800"> |
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</p> |
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This repo has the models for [CrossMAE: Rethinking Patch Dependence for Masked Autoencoders](https://arxiv.org/abs/2401.14391). |
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Please take a look at the [GitHub repo](https://github.com/TonyLianLong/CrossMAE) to see instructions on pretraining, fine-tuning, and evaluation with these models. |
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