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Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models

• Authors: Archiki Prasad, Elias Stengel-Eskin, and Mohit Bansal (UNC Chapel Hill)
• Paper
• Note: This is preliminary version of our code. The complete code to run all experiments in the paper will be added shortly.

Dependencies

This code is written using PyTorch, HuggingFace's Transformer, and Salesforce's Lavis repos. Loading large vision language models (LVLMs) within RepARe requires access to GPUs. We use Nvidia A6000 48 GB GPUs to load models with 8-bit quantization.

Installation

The simplest way to run our code is to build separate conda environments for each LVLM architecture.

For BLIP-2:

conda create -n lavis python=3.8
conda activate lavis
cd Lavis
pip install -e .

For MiniGPT-4:

cd MiniGPT-4
conda env create -f environment.yml
conda activate minigpt4

Note: Please download the pretrained LLM weights and the pretrained MiniGPT-4 checkpoint from the MiniGPT-4 repo as per their instructions. You may also need to update local paths to datasets and model checkpoints in the corresponding .yaml files in {Lavis/MiniGPT-4}/{lavis/minigpt4}/configs/ and {Lavis/MiniGPT-4}/{lavis/minigpt4}/projects/{blip2/minigpt4}/. Please also download the datasets (annotations folders) and specify the paths in the .yaml files in {Lavis/MiniGPT-4}/{lavis/minigpt4}/configs/datasets/{coco/aokvqa}.

Running RepARe

Set the arguments in the .yaml files of each project in {Lavis/MiniGPT-4}/{lavis/minigpt4}/projects/{blip2/minigpt4}/. To set the number of candidates besides the original question n-1 set num_add_candidates, to enable keyword extraction, set keyword_pipeline: True, and to use LVLM rationales set reason: True. To run the paraphrase baseline, only set paraphrase: False and to use the external paraphrase model set ext_paraphrase: True.

Here are some example run commands:

For BLIP-2:

cd Lavis
python evaluate.py --cfg-path lavis/projects/blip2/vqav2_zeroshot_flant5xl_eval.yaml

For MiniGPT-4:

cd MiniGPT-4
python evaluate.py --cfg-path minigpt4/projects/minigpt4/conv_vqav2.yaml

Acknowledgements

We thank the authors and contributors of BLIP-2, and MiniGPT-4 for their public code release.

Reference

Please cite our paper if you use our code in your works:

@article{Prasad2023RepARe,
  title         = {Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models},
  author        = {Archiki Prasad and Elias Stengel-Eskin and Mohit Bansal},
  year          = {2023},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  eprint        = {2310.05861}
}