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PWC

MFAS: Multimodal Fusion Architecture Search

This code

This is an implementation of the paper:

@inproceedings{perez2019mfas,
  title={Mfas: Multimodal fusion architecture search},
  author={P{\'e}rez-R{\'u}a, Juan-Manuel and Vielzeuf, Valentin and Pateux, St{\'e}phane and Baccouche, Moez and Jurie, Fr{\'e}d{\'e}ric},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6966--6975},
  year={2019}
}

Thanks

This is an fork repository of mfas with custom extension. Thank you very much juanmanpr for your contribution.

Usage

We focus on the NTU experiments in this repo. The file main_found_ntu.py is used to train and test architectures that were already found. Pick one of them by using the --conf N argument. This script should be easy to modify if you want to try other architectures.

Our best found architecture on NTU is slightly different to the one reported in the paper, it can be tested like so:

python main_found_ntu.py --datadir ../../Data/NTU --checkpointdir ../../Data/NTU/checkpoints/ --use_dataparallel --test_cp best_3_1_1_1_3_0_1_1_1_3_3_0_0.9134.checkpoint --conf 4 --inner_representation_size 128 --batchnorm

To test the architecture from the paper, you can run:

python main_found_ntu.py --datadir ../../Data/NTU --checkpointdir ../../Data/NTU/checkpoints/ --use_dataparallel --test_cp conf_[[3_0_0]_[1_3_0]_[1_1_1]_[3_3_0]]_both_0.896888457572633.checkpoint

Of course, set your own Data and Checkpoints directories.

What's new

I extend the repo to finish the experiment on another dataset named AVMNIST. The file avmnist_gen.py is used to generate the avmnist dataset using FSDD and ESC-50. train_image.py and train_audio.py are used to train the simple uni-modal classification models that take place of the feature extrator in MFAS. Then, you can use the file main_searchable_avmnist.py to search new fusion neural model architectures and file main_found_avmnist.py can used to evaluate the candidates.

Download the pretrained checkpoints

We provide pretrained backbones for RGB and skeleton modalities as well as some pretrained found architectures in here: Google Drive link