UniVTG (ICCV'23)

[arXiv]

TL; DR: The first video temporal grounding pretraining model, unifying diverse temporal annotations to power moment retrieval (interval), highlight detection (curve) and video summarization (point).

📢 News

• [2023.10.15] Upload the Clip teacher scripts to create scalable pseudo annotations.
• [2023.8.22] Code cleaning, add training/inference instruction, upload all downstream checkpoints.
• [2023.8.6] Create the Huggingface space demo!
• [2023.7.31] We release the arXiv paper, codes, checkpoints, and gradio demo.

📝 Todo

• Connect UniVTG with LLM e.g., ChatGPT.
• Upload all downstream checkpoints.
• Upload all pretraining checkpoints.

🌟 Run on your video:

To power practical usage, we release the following checkpoints:

can be run on a single GPU with less than 4GB memory, highly efficient, less than 1 sec to perform temporal grounding even a 10 minutes long video.

Video Enc.	Text Enc.	Pretraining	Fine-tuning	Checkpoints
CLIP-B/16	CLIP-B/16	4M	-	Google Drive
CLIP-B/16	CLIP-B/16	4M	QVHL + Charades + NLQ + TACoS + ActivityNet + DiDeMo	Google Drive

Download checkpoint and put it in the dir results/omni.

Download the example videos from here and put it under examples/

Run python3 main_gradio.py --resume ./results/omni/model_best.ckpt

[ Youtube video ]

[ Egocentric video ]

[ Charades video ]

⚙️ Preparation

Please find instructions in install.md to setup environment and datasets.

📦 Model Zoo

Download checkpoints in model.md to reproduce the benchmark results.

🚀 Training & Inference

We use slurm for job running, you may need to slightly modify the code to adapt your environment if you do not use slurm system.

Pretraining (multi-gpu)

Large-scale pretraining: bash scripts/pretrain.sh

Multi-datasets co-training: bash scripts/cotrain.sh

Downstream (single-gpu)

Indicate --resume to init model by pretraining weight. Refer to our model zoo for detailed parameter settings

Training: bash scripts/qvhl_pretrain.sh

Indicate --eval_init and --n_epoch=0 to evaluate selected checkpoint --resume.

Inference: bash scripts/qvhl_inference.sh

CLIP teacher to create scalable pseudo labels

1. Download the openimages v6 class list from https://storage.googleapis.com/openimages/v6/oidv6-class-descriptions.csv.

2. Convert it as json by python3 teacher/csv2json.py then extract the textual class features by python3 teacher/label2feature.py

3. (Before this, you should have extracted the video features of the video) Run the script to generate pseudo labels python3 teacher/clip2labels.py

🎨 Visualization

If you want to draw visualizations like our paper, you can simply run python3 plot/qvhl.py to generate corresponding figures by providing the prediction jsons (you can download them in Model Zoo).

🎓 Citation

If you find our work helps, please cite our paper.

@misc{lin2023univtg,
      title={UniVTG: Towards Unified Video-Language Temporal Grounding}, 
      author={Kevin Qinghong Lin and Pengchuan Zhang and Joya Chen and Shraman Pramanick and Difei Gao and Alex Jinpeng Wang and Rui Yan and Mike Zheng Shou},
      year={2023},
      eprint={2307.16715},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

✉️ Contact

This repo is maintained by Kevin. Questions and discussions are welcome via kevin.qh.lin@gmail.com or open an issue.

😊 Acknowledgement

This codebase is based on moment_detr, HERO_Video_Feature_Extractor, UMT.

We thank the authors for their open-source contributions.