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Open source code for paper: Multimodal Reaction: Information Modulation for Cross-modal Representation Learning

Multimodal Information Modulation

This is the open source code for paper: Multimodal Reaction: Information Modulation for Cross-modal Representation Learning

Table of Contents

• Description
• Preparation
• Running

Description

This is the open source code for paper: Multimodal Reaction: Information Modulation for Cross-modal Representation Learning. We have provided the implementation on the task of multimodal sentiment analysis. The main components are as follow:

1. ./datasets contains the datasets used in the experiments
2. ./modules contains the model definition
3. ./utils contains the functions for data processing, evaluation metrics, etc. The file ./utils/loss.py will be updated in May, 2024.
4. global_configs.py defines important constants
5. train.py defines the training process

Preparation

Datasets

We have already provided the processed MOSI dataset in ./dataset

To download the larger MOSEI dataset, you can run datasets/download_datasets.sh

Installation

To install the required packages, you can run pip install -r requirements.txt

Configuration

Before starting training, you should define the global constants in global_configs.py. Default configuration is set to MOSI dataset. Important settings include GPU setting, learning rate, feature dimension, training epochs, training batch size, dataset setting and dimension setting of input data. To run the MOSEI dataset, remember to change dimension of the visual modality

from torch import nn

class DefaultConfigs(object):

    device = '1'                                 #GPU setting
    logs = './logs/'
    
    max_seq_length = 50 
    lr = 1e-5                                    #learning rate
    d_l = 80                                     #feature dimension
    n_epochs = 100                               #training epochs
    train_batch_size = 16                        #training batch size
    dev_batch_size = 128
    test_batch_size = 128
    model_choice = 'bert-base-uncased'

    dataset = 'mosi'                             #dataset setting
    TEXT_DIM = 768                               #dimension setting
    ACOUSTIC_DIM = 74
    VISUAL_DIM = 47

    # dataset = 'mosei'
    # ACOUSTIC_DIM = 74
    # VISUAL_DIM = 35
    # TEXT_DIM = 768

config = DefaultConfigs()

Running

To run the experiments, you can run the following command

python train.py

Note that the proposed method is flexible to work with various fusion strategies. We have performed experiments on element-wise addition, element-wise multiplication, concatenation and tensor fusion. If tensor fusion is adopted, the setting of feature dimension d_l in global_configs.py can be larger to ensure higher capacity.

Acknowledgments

We would like to express our gratitude to huggingface and MAG-BERT, which are of great help to our work.

mim's Issues

代码不全

作者您好，我仔细看了您的该篇论文，对于如何动态确定主模态这块的代码很感兴趣，但是我发现代码不全，您可以补全嘛

作者您好

作者您好，上传的代码不全，能够补全吗？