ConZIC

[CVPR 2023]ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zequn Zeng, Hao Zhang, Zhengjue Wang, Ruiying Lu, Dongsheng Wang, Bo Chen

News

[2023/4] Adding demo on Huggingface Space and Colab!
[2023/3] ConZIC is publicly released!

Framework

Gibbs-BERT

Example of sentiment control

DEMO

Preparation

Please download CLIP and BERT from Huggingface Space.

SketchyCOCOcaption benchmark in our work is available here.

Environments setup.

pip install -r requirements.txt

To run zero-shot captioning on images:

ConZIC supports arbitary generation orders by change order. You can increase alpha for more fluency, beta for more image content. Notably, there is a trade-off between fluency and image-matching degree.
Sequential: update tokens in classical left to right order. At each iteration, the whole sentence will be updated.

python demo.py --run_type "caption" --order "sequential" --sentence_len 10 --caption_img_path "./examples/girl.jpg" --samples_num 1
--lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32" 
--alpha 0.02 --beta 2.0

Shuffled: update tokens in random shuffled generation order, different orders resulting in different captions.

python demo.py --run_type "caption" --order "shuffle" --sentence_len 10 --caption_img_path "./examples/girl.jpg" --samples_num 3
--lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32" 
--alpha 0.02 --beta 2.0

Random: only randomly select a position and then update this token at each iteration, high diversity due to high randomness.

python demo.py --run_type "caption" --order "random" --sentence_len 10 --caption_img_path "./examples/girl.jpg" --samples_num 3
--lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32" 
--alpha 0.02 --beta 2.0

To run controllable zero-shot captioning on images:

ConZIC supports many text-related controllable signals. For examples:
Sentiments(positive/negative): you can increase gamma for higher controllable degree, there is also a trade-off.

python demo.py 
--run_type "controllable" --control_type "sentiment" --sentiment_type "positive"
--order "sequential" --sentence_len 10 --caption_img_path "./examples/girl.jpg" --samples_num 1
--lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32" 
--alpha 0.02 --beta 2.0 --gamma 5.0

Part-of-speech(POS): it will meet the predefined POS templete as much as possible.

python demo.py 
--run_type "controllable" --control_type "pos" --order "sequential"
--pos_type "your predefined POS templete"
--sentence_len 10 --caption_img_path "./examples/girl.jpg"  --samples_num 1
--lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32" 
--alpha 0.02 --beta 2.0 --gamma 5.0

Length: change sentence_len.

Gradio Demo

We highly recommend to use the following WebUI demo in your browser from the local url: http://127.0.0.1:7860.

pip install gradio
python app.py --lm_model "bert-base-uncased" --match_model "openai/clip-vit-base-patch32"

You can also use the demo.launch() function to create a public link used by anyone to access the demo from their browser by setting share=True.

Citation

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

@article{zeng2023conzic,
  title={ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing},
  author={Zeng, Zequn and Zhang, Hao and Wang, Zhengjue and Lu, Ruiying and Wang, Dongsheng and Chen, Bo},
  journal={arXiv preprint arXiv:2303.02437},
  year={2023}
}

Contact

If you have any questions, please contact zzequn99@163.com or zhanghao_xidian@163.com.

Acknowledgment

This code is based on the bert-gen and MAGIC.

Thanks for Jiaqing Jiang providing huggingface and Colab demo.

DableUTeeF/ConZIC