Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling

TL;DR

This paper introduces Disjoint Masking with Joint Distillation (DMJD), a training scheme for masked image modeling that improves efficiency and performance by utilizing multiple masked views and dual prediction targets, reducing training time.

Contribution

The paper proposes DMJD, a novel training scheme combining disjoint masking and joint distillation to enhance training efficiency and model performance in masked image modeling.

Findings

DMJD trains ViT with half the epochs compared to traditional methods.

DMJD improves linear probing accuracy over ConvMAE by 5.8%.

DMJD achieves superior results on downstream tasks like segmentation and detection.

Abstract

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of each view. For joint distillation (JD), we adopt a dual branch architecture to respectively predict invisible (masked) and visible (unmasked) tokens with superior learning targets. Rooting in orthogonal perspectives for training efficiency improvement, DM and JD cooperatively accelerate the training convergence yet not sacrificing the model generalization ability. Concretely, DM can train ViT with half of the effective training epochs (3.7 times less time-consuming) to report competitive performance. With JD, our DMJD clearly improves the linear probing classification accuracy over ConvMAE by 5.8%. On fine-grained downstream tasks like semantic segmentation, object detection, etc., our DMJD also presents superior generalization compared with state-of-the-art SSL methods. The code and model will be made public at https://github.com/mx-mark/DMJD.