• MMIN
• Environment
• Usage
• Download the features
• License
• Citation

MMIN

This repo implements the Missing Modality Imagination Network(MMIN) for the following paper:
"Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities"

Environment

python 3.7.0
pytorch >= 1.0.0

Usage

First you should change the data folder path in data/config and preprocess your data follwing the code in preprocess/.

The preprocess of feature was done handcrafted in several steps, we will make it a automatical running script in the next update. You can download the preprocessed feature to run the code.

• For Training MMIN on IEMOCAP:

  First training a model fusion model with all audio, visual and lexical modality as the pretrained encoder.

  bash scripts/CAP_utt_fusion.sh AVL [num_of_expr] [GPU_index]
  

  Then

  bash scripts/CAP_mmin.sh [num_of_expr] [GPU_index]
  

• For Training MMIN on MSP-improv:

  bash scripts/MSP_utt_fusion.sh AVL [num_of_expr] [GPU_index]
  

  bash scripts/MSP_mmin.sh [num_of_expr] [GPU_index]
  

Note that you can run the code with default hyper-parameters defined in shell scripts, for changing these arguments, please refer to options/get_opt.py and the modify_commandline_options method of each model you choose.

Download the features

Baidu Yun Link
IEMOCAP A V L modality Features
链接: https://pan.baidu.com/s/1WmuqNlvcs5XzLKfz5i4iqQ 提取码: gn6w

Google Drive Link
https://drive.google.com/file/d/1X5wjY-eMnLPV2qkFaaRi9ZPkrMcCAv7Q/view?usp=sharing

Baidu Yun Link
MSP A V L modality Features
链接: https://pan.baidu.com/s/17E44x84pdR2AQIts0aJfKg 提取码: 6dzq

License

MIT license.

Copyright (c) 2021 AIM3-RUC lab, School of Information, Renmin University of China.

Citation

If you find our paper and this code usefull, please consider cite

@inproceedings{zhao2021missing,
  title={Missing modality imagination network for emotion recognition with uncertain missing modalities},
  author={Zhao, Jinming and Li, Ruichen and Jin, Qin},
  booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  pages={2608--2618},
  year={2021}
}