Improving Masked Autoencoders by Learning Where to Mask

• URL: http://arxiv.org/abs/2303.06583v2
• Date: Sun, 7 Jan 2024 04:17:34 GMT
• Title: Improving Masked Autoencoders by Learning Where to Mask
• Authors: Haijian Chen, Wendong Zhang, Yunbo Wang, Xiaokang Yang
• Abstract summary: Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
• Score: 65.89510231743692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them. The question is: Is there a better masking strategy than random sampling and how can we learn it? We empirically study this problem and initially find that introducing object-centric priors in mask sampling can significantly improve the learned representations. Inspired by this observation, we present AutoMAE, a fully differentiable framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In this way, our approach can adaptively find patches with higher information density for different images, and further strike a balance between the information gain obtained from image reconstruction and its practical training difficulty. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.

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