Self-Chained Image-Language Model for Video Localization and Question Answering

Authors: Shoubin Yu, Jaemin Cho, Prateek Yadav, Mohit Bansal
Paper: arXiv
Online Demo: Try our Gradio demo on Hugging Face

Code structure

# data & data preprocessing
./sevila_data

# pretrained checkpoints
./sevila_checkpoints

# SeViLA code
./lavis/

# running scripts for SeViLA localizer/answerer training/inference
./run_scripts

Setup

Install Dependencies

(Optional) Creating conda environment

conda create -n sevila python=3.8
conda activate sevila

build from source

pip install -e .

Download Pretrained Models

We pre-train SeViLA localizer on QVHighlights and hold checkpoints via Hugging Face. Download checkpoints and put it under /sevila_checkpoints. The checkpoints (814.55M) contains pre-trained localizer and zero-shot answerer.

Run Gradio Demo Locally

We also provide a UI for testing our SeViLA locally that is built with gradio. Running demo locally requires about 12GB of memory.

Installing Gradio:

pip install gradio==3.30.0

Running the following command in a terminal will launch the demo:

python app.py

Dataset Preparation

We test our model on:

Please download original QA data and preprocess them via our scripts.

Training and Inference

We provide SeViLA training and inference script examples as follows.

And please refer to dataset page to custom your data path.

1) Localizer Pre-training

sh run_scripts/sevila/pre-train/pretrain_qvh.sh

2) Localizer Self-refinement

sh run_scripts/sevila/refinement/nextqa_sr.sh

3) Answerer Fine-tuning

sh run_scripts/sevila/finetune/nextqa_ft.sh

4) Inference

sh run_scripts/sevila/inference/nextqa_infer.sh

Acknowledgments

We thank the developers of LAVIS, BLIP-2, CLIP, All-in-One, for their public code release.

Reference

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

@article{yu2023self,
  title={Self-Chained Image-Language Model for Video Localization and Question Answering},
  author={Yu, Shoubin and Cho, Jaemin and Yadav, Prateek and Bansal, Mohit},
  journal={arXiv preprint arXiv:2305.06988},
  year={2023}
}

Ziyang412/SeViLA