This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation (Guillaume Couairon, Holger Schwenk, Matthijs Douze, Matthieu Cord)

The inspiration for this work are the geometric properties of word embeddings, such as Queen ~ Woman + (King - Man). We extend this idea to multimodal embedding spaces (like CLIP), which let us semantically edit images via "delta vectors".

Transformed images can then be retrieved in a dataset of images.

We build SIMAT, a dataset to evaluate the task of text-driven image transformation, for simple images that can be characterized by a single subject-relation-object annotation. A transformation query is a pair (image, query) where the query asks to change the subject, the relation or the object in the input image. SIMAT contains ~6k images and an average of 3 transformation queries per image.

The goal is to retrieve an image in the dataset that corresponds to the query specifications. We use OSCAR as an oracle to check whether retrieved images are correct with respect to the expected modifications.

Below are a few examples that are in the dataset, and images that were retrieved for our best-performing algorithm.

The SIMAT database is composed of crops of images from Visual Genome. You first need to install Visual Genome and then run the following command :

python prepare_dataset.py --VG_PATH=/path/to/visual/genome

In this example, we use the CLIP ViT-B/32 model to edit an image. Note that the dataset of clip embeddings is pre-computed.

import clip
from torchvision import datasets
from PIL import Image
from IPython.display import display

#hack to normalize tensors easily
torch.Tensor.normalize = lambda x:x/x.norm(dim=-1, keepdim=True)

# database to perform the retrieval step
dataset = datasets.ImageFolder('simat_db/images/')

db = torch.load('data/simat_img_clip.pt')
db_stacked = torch.stack(list(db.values())).float()

idx2rid = list(db.keys())

model, prep = clip.load('ViT-B/32', device=device)

image = Image.open('simat_db/images/images/98316.png')
img_enc = model.encode_image(prep(image).unsqueeze(0).to('cuda:0')).float().cpu().detach()

txt = ['cat', 'dog']
txt_enc = model.encode_text(clip.tokenize(txt).to('cuda:0')).float().cpu().detach()

# optionally, we can apply a linear layer on top of the embeddings
heads = torch.load(f'data/head_clip_t=0.1.pt')
img_enc = heads['img_head'](img_enc)
txt_enc = heads['txt_head'](txt_enc)

db = heads['img_head'](db).normalize()


# now we perform the transformation step
lbd = 1
target_enc = img_enc.normalize() + lbd * (txt_enc[1].normalize() - txt_enc[0].normalize())


retrieved_idx = (db_stacked @ target_enc.float().T).argmax(0).item()

retrieved_rid = idx2rid[retrieved_idx]

display(Image.open(f'simat_db/images/images/{retrieved_rid}.png'))

You can run the evaluation script with the following command:

python eval.py --backbone clip --domain dev --tau 0.01 --lbd 1 2

It automatically load the adaptation layer relative to the value of tau.

In this part, you can train linear layers after the CLIP encoder on the COCO dataset, to get a better alignment. Here is an example :

python adaptation.py --backbone ViT-B/32 --lr 0.001 --tau 0.1 --batch_size 512

If you find this paper or dataset useful for your research, please use the following.

@article{gco1embedding,
  title={Embedding Arithmetic for text-driven Image Transformation},
  author={Guillaume Couairon, Matthieu Cord, Matthijs Douze, Holger Schwenk},
  journal={arXiv preprint arXiv:2112.03162},
  year={2021}
}

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision, OpenAI 2021

Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, Fei-Fei Li. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations, IJCV 2017

Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao, Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks, ECCV 2020

The SIMAT is released under the MIT license. See LICENSE for details.