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Perceiver-VL

Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention [WACV 2023 bib]

Zineng Tang*, Jaemin Cho*, Jie Lei, Mohit Bansal

Learning vision-language representation by iterative latent attention that scales with long inputs linearly.

Introduction

Perceiver-VL Architecture Overview

Install

Setup python environment

conda create -n Perceiver-VL python=3.8   # You can also use other environment.

Install other dependencies

pip install -r requirements.txt

Training

TODO: Finish datasets/tasks instructions and scripts

Pretraining (scripts)

# Pretrain on Webvid + GCC
bash scripts/co_pretrain.sh

# Pretrain on Webvid
bash scripts/webvid_pretrain.sh

# Pretrain on GCC
bash scripts/gcc_pretrain.sh

# Pretrain on ImageNet
bash scripts/imagenet_pretrain.sh

Pretrained Checkpoint

Download Checkpoint [link]

Finetuning on Downstream (scripts)

# Fintune on MSRVTT Retrieval
bash scripts/msrvtt_vrtr_finetune.sh

# Fintune on VQA
bash scripts/vqa_finetune.sh

Code Structure

Perceiver_VL
│
├── assets                          # illustrations                          
│   └── architecture.png
│
├── model                           # main source       
│   ├── datamodules                 # pytorch-lightning wrap
│   │   ├── datamodule_base.py
│   │   └── ...          
│   └── datasets                    # Datasets
│   │   ├── vqa_dataset.py     
│   │   └── ...    
│   ├── gadgets     
│   │   └── my_metrics.py           # metric utils
│   ├── modules                     
│   │   ├── heads.py                # model heads
│   │   ├── model_module.py         # pytorch-lightning wrap for model
│   │   ├── model_utils.py          # pytorch-lightning wrap for training metrics
│   │   ├── objectives.py           # pretraining/finetuning objectives
│   │   └── perceiver_vl.py         # main model
│   ├── transforms                  # image transformation utils
│   │   └── ... 
│   └── config.py                   # all configurations
│
├── scripts                         # all scripts
│   ├── vqa_finetune.sh 
│   ├── co_pretrain.sh
│   └── ... 
│
├── run.py                          # main
└── requirements.txt                

Citation

@inproceedings{tang2023wacv,
  title     = {Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention},
  author    = {Zineng Tang and Jaemin Cho and Jie Lei and Mohit Bansal},
  booktitle = {WACV},
  year      = {2023}
}

Acknowledgement

Our codebase is based on ViLT. We thank the authors for their open-source contributions.

Contact

Zineng Tang (zn.tang.terran@gmail.com)