HighMMT

HighMMT is a general-purpose model for high-modality (large number of modalities beyond the prototypical language, visual, and acoustic modalities) and partially-observable (across many tasks, where each task is defined only over a small subset of all modalities we are interested in modeling) scenarios.

HighMMT uses multitask learning with shared unimodal and multimodal layers to enable stable parameter counts (addressing scalability) and cross-modal transfer learning to enable information sharing across modalities and tasks (addressing partial observability).

The same HighMMT model (architecture and parameters) is able to simultaneously encode joint representations between different subsets spanning images, text, audio, sets, time-series, and graphs.

Paper

High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov
TMLR 2022.

If you find this repository useful, please cite our paper:

@article{liang2022high,
  title={High-Modality Multimodal Transformer: Quantifying Modality \& Interaction Heterogeneity for High-Modality Representation Learning},
  author={Liang, Paul Pu and Lyu, Yiwei and Fan, Xiang and Tsaw, Jeffrey and Liu, Yudong and Mo, Shentong and Yogatama, Dani and Morency, Louis-Philippe and Salakhutdinov, Russ},
  journal={Transactions on Machine Learning Research},
  year={2022}
}

Contributors

Correspondence to:

• Paul Pu Liang ([email protected])
• Yiwei Lyu ([email protected])
• Xiang Fan ([email protected])
• Jeffrey Tsaw ([email protected])
• Yudong Liu ([email protected])
• Shentong Mo ([email protected])
• Dani Yogatama
• Louis-Philippe Morency
• Ruslan Salakhutdinov

Usage

Environment Setup Using Conda

conda env create -f env_HighMMT.yml

Quick Start

The instructions for running the code and data retreival can be found after typing

./run.sh help

You can also find detailed instructions below

Data Download

three datasets: robotics, enrico and RTFM can be setup directly using script ./download_datasets.sh Run

./download_datasets.sh help

for instructions To setup each dataset, run "./download_datasets.sh " For example

./download_datasets.sh robotics

downloads the robotics dataset to the directory datasets/robotics This repo is built on top of the MultiBench repository, so to download the dataset, follow the same instructions as https://github.com/pliang279/MultiBench.git

Easy setting experiment code

From the root of this repo, run

python private_test_scripts/perceivers/roboticstasks.py model.pt

The model will be saved to model.pt.

Medium setting experiment code

To run medium tasks, please run

python private_test_scripts/perceivers/medium_tasks.py

Hard setting experiment code

To run multitask training on 1/2/3/4 tasks, please run

python private_test_scripts/perceivers/singletask.py
python private_test_scripts/perceivers/twomultitask.py
python private_test_scripts/perceivers/threemultitask.py
python private_test_scripts/perceivers/fourmultitask.py

Parameter Sharing Experiments

To run the parameter sharing experiments, please run

python private_test_scripts/perceivers/shared_fourmulti.py

A baseline can be trained as a starting point for finetuning by running the fourmultitask.py file like described above. You can specify the baseline in shared_fourmulti.py.

Parameter groupings can also be specified in the shared_fourmulti.py file.

Heterogeneity Matrix

To run get the heterogeneity matrix between individual modalitiesa and pairs of modalities, please run

python private_test_scripts/perceivers/tasksim.py