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Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic [CVPR 2022]

Check out our follow-up work - Zero-Shot Video Captioning with Evolving Pseudo-Tokens!

[Paper] [Notebook] [Caption Demo] [Arithmetic Demo] [Visual Relations Dataset]

⭐ New: Run captioning configuration it in the browser using replicate.ai UI.

Approach

Example of capabilities

Example of Visual-Semantic Arithmetic

Usage

To run captioning on a single image:

$ python run.py 
--reset_context_delta
--caption_img_path "example_images/captions/COCO_val2014_000000097017.jpg"

To run model on visual arithmetic:

$ python run.py 
--reset_context_delta
--end_factor 1.06
--fusion_factor 0.95
--grad_norm_factor 0.95
--run_type arithmetics
--arithmetics_imgs "example_images/arithmetics/woman2.jpg" "example_images/arithmetics/king2.jpg" "example_images/arithmetics/man2.jpg"
--arithmetics_weights 1 1 -1

To run model on real world knowledge:

$ python run.py
--reset_context_delta --cond_text "Image of" 
--end_factor 1.04 
--caption_img_path "example_images/real_world/simpsons.jpg"

To run model on OCR:

$ python run.py
--reset_context_delta --cond_text "Image of text that says" 
--end_factor 1.04 
--caption_img_path "example_images/OCR/welcome_sign.jpg"

For runtime speedup using multiple gpus, use the --multi_gpu flag:

$ CUDA_VISIBLE_DEVICES=0,1,2,3,4 python run.py 
--reset_context_delta
--caption_img_path "example_images/captions/COCO_val2014_000000097017.jpg"
--multi_gpu

Citation

Please cite our work if you use it in your research:

@article{tewel2021zero,
  title={Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic},
  author={Tewel, Yoad and Shalev, Yoav and Schwartz, Idan and Wolf, Lior},
  journal={arXiv preprint arXiv:2111.14447},
  year={2021}
}