🎬 Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-from Video Humor Detection

Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-from Video Humor Detection

Yang Liu, Tongfei Shen, Dong Zhang, Qingying Sun, Shoushan Li and Guodong Zhou

The growing importance of multi-modal humor detection within affective computing correlates with the expanding influence of short-form video sharing on social media platforms. In this paper, we propose a novel two-branch hierarchical model for short-form video humor detection (SVHD), named Comment-aided Video-Language Alignment (CVLA) via data-augmented multi-modal contrastive pre-training. Notably, our CVLA not only operates on raw signals across various modal channels but also yields an appropriate multi-modal representation by aligning the video and language components within a consistent semantic space. The experimental results on two humor detection datasets, including DY11k and UR-FUNNY, demonstrate that CVLA dramatically outperforms state-of-the-art and several competitive baseline approaches. Our dataset, code and model are available here.

🏠 Preparations

Clone this reposity:

git clone https://github.com/yliu-cs/CVLA.git
cd CVLA

The directory tree is shown as below:

CVLA
├── dataset
│   ├── labeled
│   │   ├── 6557952865950764295
│   │   │   ├── video.mp4
│   │   │   ├── comment.json
│   │   │   ├── info.json
│   │   │   └── audio.wav
│   │   └── ...
│   ├── unlabeled
│   │   ├── 6937837057259621664
│   │   │   └── ...
│   │   └── ...
│   ├── split
│   │   └── train
│   └── vid2en.pkl
├── plot
│   ├── attention.py
│   ├── duration.py
│   ├── like.py
│   ├── loss.py
│   ├── Theme.md
│   └── ...
├── tools
│   ├── gather_result.py
│   ├── split_dataset.py
│   └── translate.py
├── run.sh
├── run.py
├── param.py
├── models.py
├── data.py
├── README.md
└── requirements.txt

Download our proposed dataset DY11k from DY11k Download URL, and unzip it into dataset folder.

⚙️ Requirements

To run our code, please install all the dependency packages by using the following command:

conda create -n CVLA python=3.10
conda activate CVLA
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt

Note: Different versions of packages (like pytorch, transformers, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.

🎮 Multiple data splits

To carry out experiments with multiple data splits (aggregate the results over 5 different seeds), you can use the following scripts:

for seed in 2 42 327 2023 998244353
do
	python tools/split_dataset.py --seed $seed
done

🚀 Experiments with multiple runs

Multiple model training give a more robust measure of performance and a better estimate of the standard, you can use the following scripts:

for seed in 2 42 327 2023 998244353
do
	python run.py --seed=$seed
done

Then run the following command to gather all the results:

python tools/gather_result.py > gathered_result.log

📧 Bugs or questions?

If you have any questions related to the code or the paper, feel free to email Yang ([email protected]). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

❤️ Acknowledgment

Thanks Clover, TVLT, nanoGPT and thop for their excellent code implementations, which aided later study and are referenced in this implementation as available source code.

📜 Citation

Please cite our paper if you use CVLA in your work:

@inproceedings{conf/icmr/Liu24CVLA,
  author       = {Yang Liu and Tongfei Shen and Dong Zhang and Qingying Sun and Shoushan Li and Guodong Zhou},
  title        = {Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-form Video Humor Detection},
  booktitle    = {Proc. of ICMR},
  pages        = {442--450},
  year         = {2024}
}