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ComCLIP: Training-Free Compositional Image and Text Matching

This is the code implementation for the NAACL2024 paper: "ComCLIP: Training-Free Compositional Image and Text Matching" [Arxiv][Project Website]

Datasets

Please follow the instructions below to prepare the datasets.

  1. Winoground
    Download images and store them as datasets/winoground_images. Code includes the download of csv file.
  2. Compositional Visual Genome (ComVG)
    Download images and store them as datasets/comvg_images. Test csv file at at datasets/ComVG.csv
  3. SVO-Probe
    Download dataset and store the images as datasets/SVO-Probes. Store csv as datasets/svo-probes.csv
  4. Flickr30k
    Download images and store them as datasets/flickr30k_image (Please only select images that are in the test sets). Test pickle file is datasets/flickr30k_test.pkl.

Usage

Preparation

Please follow GRiT and detectron2 Setup and CLIP Setup first. Download grit_b_densecap_objectdet.pth and store it in GRiT/models. Please follow SLIP and download ViT-L weights in SLIP/MODEL_PATH

conda create --name comclip python=3.10
conda activate comclip
pip install -r requirements.txt
pip install git+https://github.com/openai/CLIP.git

Winoground

### clip baseline
python winoground/clip_baseline.py --huggingface_token HUGGINGFACE_TOKEN
### blip baseline
python winoground/blip_baseline.py --huggingface_token HUGGINGFACE_TOKEN
### slip baseline
python winoground/slip_baseline.py --huggingface_token HUGGINGFACE_TOKEN

### comclip 
winoground/comclip.sh datasets/winoground_images DENSE_CAPTION_PATH PARSE_TEXT_PATH GRiT_MODEL HUGGINGFACE_KEY OPENAI_KEY
### comblip
winoground/comclip.sh datasets/winoground_images DENSE_CAPTION_PATH PARSE_TEXT_PATH GRiT_MODEL HUGGINGFACE_KEY OPENAI_KEY
### comslip
winoground/comslip.sh datasets/winoground_images DENSE_CAPTION_PATH PARSE_TEXT_PATH GRiT_MODEL HUGGINGFACE_KEY OPENAI_KEY

ComVG & SVO-Probes

### clip baseline
python ComVG/clip_baseline.py --model ViT-L/14 --data_path datasets/ComVG.csv --image_path datasets/comvg_images
### comclip 
ComVG/comclip.sh datasets/comvg_images DENSE_CAPTION_PATH GRiT_MODEL_PATH datasets/ComVG.csv OPENAI_KEY ViT-L/14

Flick30k (image retrieval)

### clip baseline (precompted in datasets/flickr30k_test.pkl already)
python image_retrieval/clip_baseline.py --model VISION_ENCODER_TYPE --dataset datasets/flickr30k_test.pkl --image_path datasets/flickr30k_image
### comclip 
image_retrieval/comclip.sh datasets/flickr30k_image DENSE_CAPTION_FOLDER GRiT_MODEL_PATH datasets/flickr30k_test.pkl OPENAI_KEY VISION_ENCODER_VERSION

Acknowledgement

This code is mainly built on 1.GRiT 2.CLIP. We thank the authors for their model and code.

Citation

@article{jiang2022comclip,
  title={Comclip: Training-free compositional image and text matching},
  author={Jiang, Kenan and He, Xuehai and Xu, Ruize and Wang, Xin Eric},
  journal={arXiv preprint arXiv:2211.13854},
  year={2022}
}