MILAN

MILAN: Masked Image Pretraining on Language Assisted Representation

摘要

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 efficient 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 224×224, MILAN achieves a top-1 accuracy of 85.4% on ViTB/16, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-B/16 backbone on ADE20K dataset, outperforming previous masked pretraining results by 4 points.

使用方式

Predict imageUse the modelTrain/Test Command

from mmpretrain import inference_model

predict = inference_model('vit-base-p16_milan-pre_8xb128-coslr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Models and results

Pretrained models

模型	Params (M)	Flops (G)	配置文件	下载
milan_vit-base-p16_16xb256-amp-coslr-400e_in1k	111.91	17.58	config	model | log

Image Classification on ImageNet-1k

模型	预训练	Params (M)	Flops (G)	Top-1 (%)	配置文件	下载
vit-base-p16_milan-pre_8xb128-coslr-100e_in1k	MILAN	86.57	17.58	85.30	config	model | log
vit-base-p16_milan-pre_8xb2048-linear-coslr-100e_in1k	MILAN	86.57	17.58	78.90	config	model | log

引用

@article{Hou2022MILANMI,
  title={MILAN: Masked Image Pretraining on Language Assisted Representation},
  author={Zejiang Hou and Fei Sun and Yen-Kuang Chen and Yuan Xie and S. Y. Kung},
  journal={ArXiv},
  year={2022}
}