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Painter

README.md

Images Speak in Images:
A Generalist Painter for In-Context Visual Learning

Xinlong Wang1*,   Wen Wang1,2*,   Yue Cao1*,   Chunhua Shen2,   Tiejun Huang1,3

1BAAI,   2ZJU,   3PKU

CVPR 2023




We present Painter, a generalist model using an "image"-centric solution for in-context visual learning, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Examples of in-context inference are illustrated in the figure above, consisting of seven in-domain examples (seven rows at top) and three out-of-domain examples (three rows at bottom). Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing. In addition, Painter significantly outperforms recent generalist models on several challenging tasks.

[Paper]

Hightlights

$\color{#2F6EBA}{Images\ Speak\ in\ Images}$

  • image as the general-purpose interface
  • redefine the output spaces of vision tasks as images

$\color{#2F6EBA}{A\ Generalist\ Painter}$

  • given an input image, prediction is to inpaint the desired but missing output "image"
  • excellent performance on 7 representative vision tasks with a single generalist model

$\color{#2F6EBA}{In{-}Context\ Visual\ Learning}$

  • automatically perform vision tasks according to the input task prompts
  • even the tasks do not exist in the training data

Installation

See installation instructions.

Data

See data instructions.

We also provide a toy training dataset, with 10 samples from each required datasets. You can put it in $Painter_ROOT/toy_datasets and set DATA_PATH=toy_datasets in $Painter_ROOT/train_painter_vit_large.sh for toy experiments.

Training

Download pre-trained MAE ViT-Large model from here and update path/to/mae_pretrain_vit_large.pth in $Painter_ROOT/train_painter_vit_large.sh.

We use 8 nodes (total_bsz = 8x8x32 = 2048) for training:

bash train_painter_vit_large.sh

Evaluation

See evaluation instructions.

A pre-trained Painter is available at 🤗 Hugging Face Models. The results on various tasks are summarized below:

depth estimation semantic seg. panoptic seg.keypoint det. denoising deraining enhance.
NYU v2 ADE20k COCO 2017 COCO 2017 SIDD 5 datasets LoL
RMSE A.Rel d1 mIoU PQAP PSNR SSIM PSNR SSIM PSNR SSIM
0.288 0.080 0.950 49.9 43.4 72.1 38.66 0.954 29.42 0.867 22.34 0.872

Citation

@article{Painter,
  title={Images Speak in Images: A Generalist Painter for In-Context Visual Learning},
  author={Wang, Xinlong and Wang, Wen and Cao, Yue and Shen, Chunhua and Huang, Tiejun},
  journal={arXiv preprint arXiv:2212.02499},
  year={2022}
}

Acknowledgement

MAE, BEiT, detectron2, Mask2Former, bts, mmcv, mmdetetection, mmpose, MIRNet, MPRNet, and Uformer.

Contact

We are hiring at all levels at BAAI Vision Team, including full-time researchers, engineers and interns. If you are interested in working with us on foundation model, visual perception and multimodal learning, please contact Xinlong Wang ([email protected]) and Yue Cao ([email protected]).