BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval

This is the official implementation for the paper BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval

BiVLC dataset

Dataset Summary

BiVLC is a benchmark for Bidirectional Vision-Language Compositionality evaluation. Each instance consists of two images and two captions. Using each of the images and captions as a base, a model is asked to select the pair that correctly represents the base versus the hard negative distractor with minor compositional changes. Thus, we can measure image-to-text and text-to-image retrieval with hard negative pairs. To obtain good results on the dataset, it is necessary that the model performs well in both directions for the same instance.

Dataset instances

Each instance of the dataset consists of six fields:

image: COCO 2017 validation image.
caption: COCO 2017 validation text describing the COCO image.
negative_caption: Negative caption generated from the COCO 2017 validation text description by SugarCrepe.
negative_image: Negative image generated from the negative caption by BiVLC.
type: Category of the negative instances: Replace, Swap or Add.
subtype: Subcategory of the negative instances: Object, Attribute or Relation.

How to use

To load data with datasets:

>>> data = load_dataset("imirandam/BiVLC", split = "test")

Instance example

Each instance has the following structure:

{
    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x332 at 0x7F9BFC0C5430>,
    'caption': 'A man throwing a ball while smiling and on a field.',
    'negative_caption': 'A man throwing a ball while a child is smiling on a field.',
    'negative_image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F9BE45571C0>,
    'type': 'add',
    'subtype': 'obj',
}

Dataset statistics

test: 2,933 instances formed by 2 images and 2 captions. 11,732 retrieval instances, 50% text-to-image and 50% image-to-text.

Source Data

image and caption are from COCO 2017 validation split.
negative_caption is a text description generated from the COCO caption by SugarCrepe.

Dataset curation

Step 1 - Uniformly format positive and hard negative captions

Step 2 - Generate hard negative images

Step 3 - Ask to human annotators to choose the best generated image

Step 4 - Filter ambiguous instances

More examples

Training Data

If you need training and validation data, you can use the datasets proposed in the paper in the following links, TROHN-Text and TORHN-Img.

Results

Results for SOTA models on SUGARCREPE and BIVLC. For the later, we provide the three metrics I2T, T2I and Group score. Human and random performance are also depicted. Bold for the best of each model family (contrastive and generative).

Findings

Contrary to humans, current models underperform on text-to-image retrieval.
The gap to humans is bigger in BiVLC than in SugarCrepe.
SugarCrepe and BIVLC performance are not correlated.

Exploring training strategies for vision-language compositionality

We propose two new models:

CLIP_TROHN-TEXT using hard negative texts.
CLIP_TROHN-IMG using hard negative texts and images.

Finding: Training with hard negative images can boost the performance of multimodal contrastive models.

Code Structure

.
├── source_data                               # Directory for source data
│   ├── COCO_2017                             # Folder to save COCO 2017
│   └── SugarCrepe                            # Folder to save SugarCrepe
│      └── concat_SugarCrepe.py               # SugarCrepe 7 json files into 1 csv
├── src                                       # Data generation and training code
│   ├── BiVLC_Generation                      # BiVLC data generation
│   │   ├── imgs                              # Folder to save the generated imgs
│   │   └── BiVLC_img_generation.py           # Image generation
│   ├── Training_data                         # Train and val data generation
│   │   ├── TROHN-Text                        # TROHN-Text data generation
│   │   │   ├── data                          # Folder to save generated negative captions
│   │   │   └── TROHN_Text_generation.py      # Negative caption generation 
│   │   └── TROHN-Img                         # TROHN-Img data generation
│   │       ├── data                          # Folder to save the scored negative captions
│   │       ├── imgs                          # Folder to save generated negative images
│   │       ├── adversarial_refine.py         # Adversarial refinement from SugarCrepe
│   │       ├── TROHN_Img_scoring.py          # Scoring TROHN-Text negative captions
│   │       ├── TROHN_Img_best_selection.py   # Selecting best negative captions
│   │       └── TROHN_Img_generation.py       # Image generation
│   ├── CLIP_fine_tuning                      # CLIP fine-tuning code and ckpts
│   │   ├── ckpt                              # Folder to save the CLIP Checkpoints
│   │   ├── CLIP_fine_tuning.py               # CLIP fine-tuning
│   │   ├── CLIP_utils.py                     # load model, load data, evaluations
│   │   └── scheduler.py                      # Cosine scheduler from OpenCLIP
│   └── Detectors                             # Detectors training and evaluation
│       ├── results                           # Detector results
│       ├── classifier_ckpt                   # Folder to save the classifier checkpoints
│       ├── Detector_img_classifier.py        # Training img detector
│       ├── Detector_text_classifier.py       # Training text detector
│       ├── Detector_evaluation_BiVLC.py      # BiVLC detector evaluation
│       ├── Detector_evaluation_SugarCrepe.py # SugarCrepe detector evaluation
│       └── Detector_utils.py                 # load model, load data
├── main_evaluation_BiVLC.py                  # Contrastive models main evaluation 
├── evaluation_SugarCrepe.py                  # Contrastive models SugarCrepe evaluation
├── VQAScore_BiVLC.py                         # VQAScore models main evaluation 
├── VQAScore_SugarCrepe.py                    # VQAScore models SugarCrepe evaluation
├── OSCapPa_BiVLC.py                          # OSCapPa model main evaluation 
├── OSCapPa_SugarCrepe.py                     # OSCapPa model SugarCrepe evaluation
├── results                                   # BiVLC and SugarCrepe results
└── requirements.txt                          # List of libraries needed

Preparing environment, data and checkpoints

Create Python Environment

git clone https://github.com/IMirandaM/BiVLC.git

cd BiVLC

python3 -m venv BiVLC              # Create env
source BiVLC/bin/activate          # Load env

pip3 install -r requirements.txt   # Install dependencies

Download source data

This section provides instructions for downloading all the data needed to reproduce the results of the paper.

IMPORTANT: If you want to evaluate only BiVLC there is no need to download any data. If you want to evaluate SugarCrepe you need to download the SugarCrepe json files and the COCO 2017 Val images. If you want to reproduce the whole implementation, download all the data.

COCO 2017

Download the COCO 2017 data from the official website, https://cocodataset.org/#download.

Download the COCO 2017 Train/Val annotations and save captions_train2017.json in the BiVLC/source_data/COCO_2017 directory.
Download the COCO 2017 train and val images and save the unzipped folders in the data_source/COCO_2017 directory as train2017 and val2017.

Resulting in:

├── source_data                               
│   └── COCO_2017                              
│       ├── captions_train2017.json           
│       ├── train2017
│       └── val2017

SugarCrepe

Download SugarCrepe data in BiVLC/source_data/SugarCrepe from the official repository, https://github.com/RAIVNLab/sugar-crepe/tree/main/data.

Then we concatenate the 7 json files into a single CSV.

python source_data/SugarCrepe/concat_SugarCrepe.py

Download checkpoints

Model checkpoints are hosted in HuggingFace repositories. You only need to download the .pt files.

CLIP Fine-tuning

We have fine-tuned three different CLIP models. Download the .pt files from the following links, we recommend saving them in the following folder src/CLIP_fine_tuning/ckpt.

Detectors

We have trained two detectors. Download the best_image and best_text .pt files from the following links, we recommend saving them in the following folder src/Detectors/classifier_ckpt.

NOTE: Alternatively you can download the checkpoints using HuggingFace-hub. For example:

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="imirandam/CLIP_COCO", filename="best_CLIP_COCO_9.pt", local_dir = "./src/CLIP_fine_tuning/ckpt")

repo_id = HuggingFace repository

filename = checkpoint_name.pt

local_dir = directory to save the checkpoint

Evaluation

This section will show how to reproduce evaluations in BiVLC and SugarCrepe datasets.

Main evaluation BiVLC

Contrastives

python main_evaluation_BiVLC.py \
   --model-checkpoint 'src/CLIP_fine_tuning/ckpt/best_CLIP_TROHN-Img_9.pt' \
   --run_name 'CLIP_TROHN-Img'

Note: In the above example we only evaluate CLIP_TROHN-Img, to evaluate any other model just change the --model-checkpoint argument and add a --run_name to identify it.

We provide the checkpoints of our models in HuggingFace repositories (See Download checkpoints section above), for NegCLIP and GNM models you should download their checkpoints directly from their official repositories. To evalute the baseline model simply do not add the --model-checkpoint argument.

Generatives

Open-source CapPa

To evaluate this model you will need to prepare the environment as described in the official repository CLIP-JAX, and also install the datasets library.

python OSCapPa_BiVLC.py \
   --local_dir 'Define path'

Note In --local_dir Define the folder where you want to save the model

VQA-Score models

To evaluate this model you will need to prepare the environment as described in the official repository t2v_metrics, and also install the datasets library.

python VQAScore_BiVLC.py \
   --model 'clip-flant5-xxl'

Note: In the above example we evaluate clip-flant5-xxl, change the --model argument to 'clip-flant5-xl' to evaluate the XL model.

Evaluate in SugarCrepe

Contrastives

To evaluate the different models in SugarCrepe change the model checkpoints and run names as in the BiVLC evaluation.

python evaluation_SugarCrepe.py \
   --model-checkpoint 'src/CLIP_fine_tuning/ckpt/best_CLIP_TROHN-Img_9.pt' \
   --run_name 'CLIP_TROHN-Img'

Generatives

Open-source CapPa

To evaluate this model you will need to prepare the environment as described in the official repository CLIP-JAX.

python OSCapPa_SugarCrepe.py \
   --local_dir 'Define path'

Note In --local_dir Define the folder where you want to save the model

VQA-Score models

To evaluate this model you will need to prepare the environment as described in the official repository t2v_metrics.

python VQAScore_SugarCrepe.py \
   --model 'clip-flant5-xxl'

Note: In the above example we evaluate clip-flant5-xxl, change the --model argument to 'clip-flant5-xl' to evaluate the XL model.

Instructions for replicating data generation, CLIP fine-tuning and Detectors

In this section we present instructions for replicating the data generation, fine-tuning of the CLIP models and training of the detectors.

Data generation

The data generation is divided into two parts, on the one hand the generation of the BiVLC benchmark and on the other hand, the training data sets, TROHN-Text and TROHN-Img.

BiVLC

To create BiVLC we relied on SugarCrepe negative captions and created 4 images for each caption with SDXL-DPO model. Then, through two phases of crowdsourcing, we kept the best images (see dataset curation section).

accelerate launch --num_processes=4 src/BiVLC_Generation/BiVLC_img_generation.py

Note: We have used 4 GPUs for the execution, if you want to use a different number modify --num_processes= number of GPUs.

TROHN-Text

To create TROHN-Text we have used the COCO 2017 train captions, the LLM OpenChat 3.5-0106 and the templates provided by Sugarcrepe. We have created a negative caption for the proposed subcategories in SugarCrepe for each of the COCO 2017 train captions.

python src/Training_data/TROHN_Text/TROHN_Text_generation.py

TROHN-Img

To create TROHN-Img we used the negative captions generated for TROHN-Text. As image generation requires a lot of computational power and time, we filtered the negative captions based on plausibility and linguistic acceptability scores to obtain the best ones.

python src/Training_data/TROHN_Img/TROHN_Img_scoring.py
python src/Training_data/TROHN_Img/TROHN_Img_best_selection.py

Once we have the best captions, we generate an image for each caption with the SDXL-DPO model.

accelerate launch --num_processes=6 src/Training_data/TROHN_Img/TROHN_Img_generation.py

Note: We have used 6 GPUs for the execution, if you want to use a different number modify --num_processes= number of GPUs.

CLIP fine-tuning

We have fine-tuned 3 CLIP models with different data, COCO 2017 train, TROHN-Text and TROHN-Img.

python src/CLIP_fine_tuning/CLIP_fine_tuning.py \
   --dataset 'COCO' --run_name 'CLIP_COCO'

Note: In the above code, we fine-tuned CLIP with the COCO 2017 training dataset, change --dataset to 'TROHN-Text' or 'TROHN-Img' for the other two fine-tunings. Also, change --run_name to CLIP_TROHN-Text or CLIP_TROHN-Img to be able to identify the checkpoints correctly.

Detectors

Training

We have trained natural vs synthetic image and text detectors. To train the text detector.

python src/Detectors/Detector_text_classifier.py \
   --model-checkpoint 'src/CLIP_fine_tuning/ckpt/best_CLIP_TROHN-Img_9.pt' \
   --run_name 'CLIP_TROHN-Img'

To train the image detector.

python src/Detectors/Detector_img_classifier.py \
   --model-checkpoint 'src/CLIP_fine_tuning/ckpt/best_CLIP_TROHN-Img_9.pt' \
   --run_name 'CLIP_TROHN-Img'

Note: To train with the baseline model encoders simply do not add the --model-checkpoint argument.

python src/Detectors/Detector_text_classifier.py \
python src/Detectors/Detector_img_classifier.py \

Evaluate in BiVLC

We evaluated the detectors in BiVLC.

python src/Detectors/Detector_evaluation_BiVLC.py \
   --image-checkpoint 'src/Detectors/classifier_ckpt/best_image_CLIP_TROHN-Img_0.pt' \
   --text-checkpoint 'src/Detectors/classifier_ckpt/best_text_CLIP_TROHN-Img_6.pt' \
   --run_name 'CLIP_TROHN-Img'

Note: In the previous example we evaluated the model based on the CLIP_TROHN-Img encoders, to evaluate the detector with the baseline encoders change the image-checkpoint and text-checkpoint provides in the HuggingFace repository (See Download checkpoints section above).

python src/Detectors/Detector_evaluation_BiVLC.py \
   --image-checkpoint 'src/Detectors/classifier_ckpt/best_image_VIT-B-32_0.pt' \
   --text-checkpoint 'src/Detectors/classifier_ckpt/best_text_VIT-B-32_7.pt' \
   --run_name 'CLIP_baseline'

Evaluate in SugarCrepe

We evaluated text detectors in SugarCrepe. To evaluate detector CLIP_TROHN-Img.

python src/Detectors/Detector_evaluation_SugarCrepe.py \
   --text-checkpoint 'src/Detectors/classifier_ckpt/best_text_CLIP_TROHN-Img_6.pt' \
   --run_name 'CLIP_TROHN-Img'

As when evaluating in BiVLC, to evaluate the detector with the baseline model encoders just change the text-checkpoint and the run_name.

python src/Detectors/Detector_evaluation_SugarCrepe.py \
   --text-checkpoint 'src/Detectors/classifier_ckpt/best_text_VIT-B-32_7.pt' \
   --run_name 'CLIP_baseline'

License

See the LICENSE file for details about the license under which code and data is made available.

Citation

If you find this repository useful in your research, please consider giving a star ⭐ and a citation

@misc{miranda2024bivlc,
      title={BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval}, 
      author={Imanol Miranda and Ander Salaberria and Eneko Agirre and Gorka Azkune},
      year={2024},
      eprint={2406.09952},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Name		Name	Last commit message	Last commit date
Latest commit History 80 Commits
misc		misc
results		results
source_data		source_data
src		src
LICENSE		LICENSE
OSCapPa_BiVLCl.py		OSCapPa_BiVLCl.py
OSCapPa_SugarCrepe.py		OSCapPa_SugarCrepe.py
README.md		README.md
VQAScore_BiVLC.py		VQAScore_BiVLC.py
VQAScore_SugarCrepe.py		VQAScore_SugarCrepe.py
evaluation_SugarCrepe.py		evaluation_SugarCrepe.py
main_evaluation_BiVLC.py		main_evaluation_BiVLC.py
requirements.txt		requirements.txt

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BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval

BiVLC dataset

Dataset Summary

Dataset instances

How to use

Instance example

Dataset statistics

Source Data

Dataset curation

More examples

Training Data

Results

Findings

Exploring training strategies for vision-language compositionality

Code Structure

Preparing environment, data and checkpoints

Create Python Environment

Download source data

COCO 2017

SugarCrepe

Download checkpoints

CLIP Fine-tuning

Detectors

Evaluation

Main evaluation BiVLC

Contrastives

Generatives

Open-source CapPa

VQA-Score models

Evaluate in SugarCrepe

Contrastives

Generatives

Open-source CapPa

VQA-Score models

Instructions for replicating data generation, CLIP fine-tuning and Detectors

Data generation

BiVLC

TROHN-Text

TROHN-Img

CLIP fine-tuning

Detectors

Training

Evaluate in BiVLC

Evaluate in SugarCrepe

License

Citation

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