MimCo: Masked Image Modeling Pre-training with Contrastive Teacher

Qiang Zhou; Chaohui Yu; Hao Luo; Zhibin Wang; Hao Li

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arXiv · 2209.03063

MimCo: Masked Image Modeling Pre-training with Contrastive Teacher

Qiang Zhou·Chaohui Yu·Hao Luo·Zhibin Wang·Hao Li

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Abstract

Recent masked image modeling (MIM) has received much attention in self-supervised learning (SSL), which requires the target model to recover the masked part of the input image. Although MIM-based pre-training methods achieve new state-of-the-art performance when transferred to many downstream tasks, the visualizations show that the learned representations are less separable, especially compared to those based on contrastive learning pre-training. This inspires us to think whether the linear separability of MIM pre-trained representation can be further improved, thereby improving the pre-training performance. Since MIM and contrastive learning tend to utilize different data augmentations and training strategies, combining these two pretext tasks is not trivial. In this work, we propose a novel and flexible pre-training framework, named MimCo, which combines MIM and contrastive learning through two-stage pre-training. Specifically, MimCo takes a pre-trained contrastive learning model as the teacher model and is pre-trained with two types of learning targets: patch-level and image-level reconstruction losses. Extensive transfer experiments on downstream tasks demonstrate the superior performance of our MimCo pre-training framework. Taking ViT-S as an example, when using the pre-trained MoCov3-ViT-S as the teacher model, MimCo only needs 100 epochs of pre-training to achieve 82.53% top-1 finetuning accuracy on Imagenet-1K, which outperforms the state-of-the-art self-supervised learning counterparts.

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BibTeX RIS

Qiang Zhou, Chaohui Yu, Hao Luo, Zhibin Wang, Hao Li. 2023-04-20. MimCo: Masked Image Modeling Pre-training with Contrastive Teacher. https://arxiv.org/abs/2209.03063

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