TAILOR

Pytorch implementation for Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition

Paper

Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition

To be published in AAAI 2022

Please cite our paper if you find our work useful for your research:

@misc{zhang2022tailor,
      title={Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition}, 
      author={Yi Zhang and Mingyuan Chen and Jundong Shen and Chongjun Wang},
      year={2022},
      eprint={2201.05834},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Overview

Overall Architecture for TAILOR

TAILOR comprises three modules: Unimodal Extractor, Adversarial Multi-modal Refinement and Label-Modal Alignment. Unimodal Extractor is designed to extract the visual features, audio features and text features with sequence level context separately. Adversarial Multi-modal Refinement is designed to extract common and private representations collaboratively. Label-Modal Alignment is designed to gradually fuse these representations in a granularity descent way and incorporated with label semantics to generate tailored label representation.

Usage

Datasets

CMU-MOSEI	Aligned	UnAligned

Note that since the labels in the unaligned data are single-label, we only use the features, and the labels are obtained from the aligned data

Model

the checkpoint for aligned data is here

get started

1. the first step is clone this repo

git clone git@github.com:kniter1/TAILOR.git

2. Set up the environment (need conda prerequisite)

conda create -n env_name python==3.7
bash init.sh

3. Modify the data path in train.sh and start training

bash train.sh

4. If you want to load the trained model for inference, you can:

bash inference.sh

Note that modify the model path and data path

Using CTC to train UnAligned data

If you want to train unaligned data, plesase install warp-ctc from here.

The quick version:

git clone https://github.com/SeanNaren/warp-ctc.git
cd warp-ctc
mkdir build; cd build
cmake ..
make
cd ../pytorch_binding
python setup.py install
export WARP_CTC_PATH=/home/xxx/warp-ctc/build

then add the following to model.py:

from warpctc_pytorch import CTCLoss

Acknowledgement

Some portion of the code were adapted from the UniVL repo. We thank the authors for their wonderful open-source efforts.