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X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers (EMNLP 2020)

• Authors: Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, and Ani Kembhavi
• Paper
• Blog
• Demo
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Summary

Recent multi-modal transformers have achieved tate of the art performance on a variety of multimodal discriminative tasks like visual question answering and generative tasks like image captioning. This begs an interesting question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family - LXMERT - finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements. X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT.

Demo

Try out AI2 Computer Vision Explorer Demo!

Install

• Python packages

conda create -n xlxmert python=3.7
conda activate xlxmert
cd  x-lxmert
pip install -r ./requirements.txt

• Mask-RCNN-benchmark (for feature extraction)

  • Please follow the original installation guide.

• Faiss (for K-means clustering)

  • Please follow the original installation guide.

Code structure

# Store images, features, and annotations
./datasets
    COCO/
        images/
        featuers/
    VG/
        images/
        features/
    GQA/
        images/
        features/
    nlvr2/
        images/
        features/
    data/               <= Store text annotations (*.json) for each split
        lxmert/
        vqa/
        gqa/
        nlvr2/

# Run feature extraction and k-means clustering
./feature_extraction

# Train image generator
./image_generator
    snap/       <= Store image generator checkpoints
    scripts/    <= Bash scripts for training image generator

# Train X-LXMERT
./x-lxmert
    src/
        lxrt/           <= X-LXMERT model class implementation (inherits huggingface transformers' LXMERT class)
        pretrain/       <= X-LXMERT Pretraining
        tasks/          <= Fine-tuning on downstream tasks (VQA, GQA, NLVR2, Image generation)
    snap/       <= Store X-LXMERT checkpoints
    scripts/    <= Bash scripts for pretraining, fine-tuning, and image generation

Feature extraction

Please checkout ./feature_extraction for download pre-extracted features and more details.

cd ./feature_extraction

# For Pretraining / VQA
python coco_extract_grid_feature.py --split train
python coco_extract_grid_feature.py --split valid
python coco_extract_grid_feature.py --split test

# For Pretraining
python VG_extract_grid_feature.py

# For GQA
python GQA_extract_grid_feature.py

# For NLVR2
python nlvr2_extract_grid_feature.py --split train
python nlvr2_extract_grid_feature.py --split valid
python nlvr2_extract_grid_feature.py --split test

# K-Means clustering
python run_kmeans.py --src mscoco_train --tgt mscoco_train mscoco valid vg

Pretraining

Pretrain on LXMERT Pretraining data

cd ./x-lxmert/
bash scripts/pretrain.bash

or download pretrained checkpoint

wget -O x-lxmert/snap/pretrained/x_lxmert/Epoch20_LXRT.pth https://ai2-vision-x-lxmert.s3-us-west-2.amazonaws.com/x-lxmert/Epoch20_LXRT.pth

Finetuning

VQA

cd ./x-lxmert/
bash scripts/finetune_vqa.bash
bash scripts/test_vqa.bash

GQA

cd ./x-lxmert/
bash scripts/finetune_gqa.bash
bash scripts/test_gqa.bash

NLVR2

cd ./x-lxmert/
bash scripts/finetune_nlvr2.bash
bash scripts/test_nlvr2.bash

Image generation

Train image generator on MS COCO

cd ./image_generator/
bash scripts/train_generator.bash

or download pretrained checkpoints

wget -O image_generator/snap/pretrained/G_60.pth https://ai2-vision-x-lxmert.s3-us-west-2.amazonaws.com/image_generator/G_60.pth

Sample images

cd ./x-lxmert/
bash scripts/sample_image.bash

Reference

@inproceedings{Cho2020XLXMERT,
  title={X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers},
  author={Cho, Jaemin and Lu, Jiasen and Schwenk, Dustin and Hajishirzi, Hannaneh and Kembhavi, Aniruddha},
  booktitle={EMNLP},
  year={2020}
}