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arXiv · 2308.08884

SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations

Zhiming Wang·Lin Gu·Feng Lu
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Abstract

Due to the prevalence of scale variance in nature images, we propose to use image scale as a self-supervised signal for Masked Image Modeling (MIM). Our method involves selecting random patches from the input image and downsampling them to a low-resolution format. Our framework utilizes the latest advances in super-resolution (SR) to design the prediction head, which reconstructs the input from low-resolution clues and other patches. After 400 epochs of pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to capture scale invariance representation. For the very low resolution (VLR) recognition task, our model achieves the best performance, surpassing DeriveNet by 1.3%. Our method also achieves an accuracy of 74.84% on the task of recognizing low-resolution facial expressions, surpassing the current state-of-the-art FMD by 9.48%.

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Zhiming Wang, Lin Gu, Feng Lu. 2023-08-17. SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations. https://arxiv.org/abs/2308.08884

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