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MultiMAE: Multi-modal Multi-task Masked Autoencoders
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2026-03-02 16:04:42
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MultiMAE | Multi-modal Multi-task Masked Autoencoders MultiMAE : Multi-modal Multi-task Masked Autoencoders Roman Bachmann * , David Mizrahi * , Andrei Atanov , Amir Zamir Swiss Federal Institute of Technology Lausanne (EPFL) * Equal contribution ECCV 2022 Paper Code Colab 🤗 Demo Poster Slides We introduce Multi-modal Multi-task Masked Autoencoders ( MultiMAE ), an efficient and effective pre-training strategy for Vision Transformers. Given a small random sample of visible patches from multiple modalities, the MultiMAE pre-training objective is to reconstruct the masked-out regions. Abstract We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders ( MultiMAE ). It differs from standard Masked Autoencoding in two key aspects: It can optionally accept additional modalities of information in the input beside RGB i...
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