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Adapting CLIP For Phrase Localization Without Further Training

Jiahao Li, Greg Shakhnarovich, Raymond A. Yeh<br/> Toyota Technological Institute at Chicago (TTIC)<br/>

The repository contains Pytorch implementation of Adapting CLIP For Phrase Localization Without Further Training. If you used this code for your experiments or found it helpful, please consider citing the following paper:

Dependencies

Following CLIP's installation procedure

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ pip install git+https://github.com/openai/CLIP.git

Specifically, we are using commit 40f5484.

Data

All of the data should be put in a data directory in the root dir of the repo.

1. Download the Flickr and VG images, to data/flickr and data/vg respectively.
2. Download Flickr30k Entities annotations using cd data/flickr && git clone https://github.com/BryanPlummer/flickr30k_entities.git.
3. Download the ZSG annotations from this link to data/ds_csv_ann:

After setting up, the data dir should have the following structure

data
data
├── flickr
  ├── flickr30k_entities
      ├── Annotations
      ├── Sentences
      ├── test.txt
      ├── train.txt
      └── val.txt
  └── flickr30k_images
├── vg
  ├── VG_100K
  └── VG_100K_2
└── ds_csv_ann
  ├── flickr30k
  ├── flickr30k_c0
  ├── flickr30k_c1
  └──vg_split

Usage

To run evaluation on the ZSG dataset as reported in the paper, please refer to a full list of arguments in eval.py to specify the dataset, architecture, etc.

For example, the following command runs ViT-L/14 architecture on the first 500 examples of the Flickr S1 validation set with an IoU threshold of 0.5.

python eval.py --model vit14 --dataset flickr_s1_val --iou_thr 0.5 --num_samples 500

Results