NFNet

NHFNet: A Non-Homogeneous Fusion Network for Multimodal Sentiment Analysis

Fusion technology is the crucial for multimodal sentiment analysis. Recent attention-based fusion methods demonstrate high performance and strong robustness. However, these approaches ignore the difference in information density among the three modalities, with visual and audio having low-level signal features and conversely text having high-level semantic features. To this end, we propose a non-homogeneous fusion network (NHFNet) to achieve multimodal information interaction. Specifically, a fusion module with attention aggregation is designed to handle the fusion of visual and audio modalities to make them have high-level semantic features. Then, cross-modal attention is used to achieve information reinforcement of text modality and audio-visual fusion. NHFNet compensates for the differences in information density of different modalities enabling their fair interaction. To verify the effectiveness of the proposed method, we set up the aligned and unaligned experiments on the CMU-MOSEI dataset, respectively. The experimental results show that the proposed method outperforms the state-of-the-art.

Cite this article

Ziwang Fu, Feng Liu, Qing Xu, iayin Qi, Xiangling Fu, Aimin Zhou, Zhibin Li. "NHFNET: A Non-Homogeneous Fusion Network for Multimodal Sentiment Analysis," 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, pp. 1-6, doi: 10.1109/ICME52920.2022.9859836

Setup

Install dependencies for CMU-MultimodalSDK

pip install h5py validators tqdm numpy argparse requests colorama

Install library for pretrained BERT model

pip install pytorch_pretrained_bert

Our dependencies

pip install sklearn

Download and preprocess the dataset with the following

python dataset_prep.py --datadir <path/to/CMU_MOSEI>

Generated folder structure (do not modify file names)

CMU_MOSEI/  # based on --datadir argument
    csd/  # can delete this folder to save space
        .csd files
    train/
        .npy files
    val/
        .npy files
    test/
        .npy files

Evaluate

python main_msaf.py --datadir <path/to/CMU_MOSEI> --checkpoint result/mosei_aligned.pth

Train

Basic training command

python main_msaf.py --datadir <path/to/CMU_MOSEI> --train

All parameters

usage: main_msaf.py [-h] [--datadir DATADIR] [--lr LR]
                    [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS]
                    [--epochs EPOCHS] [--checkpoint CHECKPOINT]
                    [--checkpointdir CHECKPOINTDIR] [--no_verbose]
                    [--log_interval LOG_INTERVAL] [--no_save] [--train]

Reference

• Note that some codes references MSAF