Prior Knowledge Integration via LLM Encoding and Pseudo-Event Regulation for Video Moment Retrieval

Yiyang Jiang, Wengyu Zhang, Xulu Zhang, Xiao-Yong Wei, Chang Wen Chen, and Qing Li.

Official Pytorch Implementation of 'Prior Knowledge Integration via LLM Encoding and Pseudo-Event Regulation for Video Moment Retrieval'

Installation | Dataset | Training | Evaluation | Model Zoo

📢 News

[2024.7.21] Our paper has been accepted by ACM Multimedia 2024 (Oral).

[2024.7.10] The code and dataset of related tasks has been released.

[2024.5.10] The repository is public.

[2024.4.10] The repository is created.

⚙️ Installation

1. Clone the repository from GitHub.

git clone https://github.com/fletcherjiang/LLMEPET.git
cd LLMEPET

2. Create conda environment.

conda create -n LLMEPET python=3.8
conda activate LLMEPET

3. Download the packages

pip install -r requirements.txt

🗂️ Dataset

For all datasets, we provide extracted features, download them and place them into features/

• QVHighlights
• Charades-STA
• TACoS
• YouTube Highlights
• TVSum

The prepared dataset should be in the following structure.

.
├── LLMEPET
│   ├── llm_epet
│   └── data
│   └── results
│   └── run_on_video
│   └── standalone_eval
│   └── utils
├── data
├── features
│   └── qvhighlight
│   └── charades
│   └── tacos
│   └── tvsum
│   └── youtube_uni
├── llama
│   └── consolidated.00.pth
│   └── tokenizer.model
│   └── params.json
├──README.md
└── ···

🪐 LLaMA Checkpoint

• You can download from huggingface OR
• LLaMA

If you want to try LLaMA-2 or LLaMA-3, you could download the checkpoints from LLaMA-2 or LLaMA-3. You should edit the (llm_epet/llama.py) by yourself.

🚀 Training

QVHighlights Training

bash llm_epet/scripts/train.sh  

Charades-STA

bash llm_epet/scripts/charades_sta/train.sh

TACoS

bash llm_epet/scripts/tacos/train.sh  

TVSum

bash llm_epet/scripts/tvsum/train_tvsum.sh  

Youtube-hl

bash llm_epet/scripts/youtube_uni/train.sh  

⭐ QVHighlights Evaluation and Submission

bash llm_epet/scripts/inference.sh results/{direc}/model_best.ckpt 'val'
bash llm_epet/scripts/inference.sh results/{direc}/model_best.ckpt 'test'

Pack the hl_{val,test}_submission.jsonl files and submit them to CodaLab.

📦 Model Zoo

Dataset	Model file
QVHighlights (Slowfast + CLIP)	checkpoints
Charades (Slowfast + CLIP)	checkpoints
TACoS	checkpoints
TVSum	checkpoints
Youtube-HL	checkpoints

📖 Citation

If you find the repository or the paper useful, please use the following entry for citation.

@inproceedings{
jiang2024prior,
title={Prior Knowledge Integration via {LLM} Encoding and Pseudo Event Regulation for Video Moment Retrieval},
author={Yiyang Jiang and Wengyu Zhang and Xulu Zhang and Xiaoyong Wei and Chang Wen Chen and Qing Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://arxiv.org/abs/2407.15051}
}