MILAN: Masked Image Pretraining on Language Assisted Representation

TL;DR

MILAN introduces a novel masked image pretraining method that leverages language-assisted semantic features from captions, leading to improved transfer learning performance in vision tasks.

Contribution

The paper proposes MILAN, a new masked image pretraining approach using caption-based semantic features, with a specialized decoder and mask sampling, outperforming previous methods.

Findings

Achieves 85.4% top-1 accuracy on ImageNet-1K with ViT-Base.

Outperforms previous masked pretraining methods in semantic segmentation by 4 mIoU points.

Demonstrates superior transfer learning capabilities across vision tasks.

Abstract

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose masked image pretraining on language assisted representation, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more effective prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224x224, MILAN achieves a top-1 accuracy of 85.4% on ViT-Base, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-Base on ADE20K dataset, outperforming previous masked pretraining results by 4 points.