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$\boldsymbol{R^2}$-Tuning

Installation | Dataset | Training | Evaluation | Model Zoo

This repository maintains the official implementation of the paper $\boldsymbol{R^2}$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding by Ye Liu, Jixuan He, Wanhua Li, Junsik Kim, Donglai Wei, Hanspeter Pfister, and Chang Wen Chen.

🔥 News

• [2024.7.2] Our paper has been accepted by ECCV 2024.
• [2024.6.16] Check out our online demo on 🤗 Hugging Face Spaces.
• [2024.6.15] Add support for single video inference.
• [2024.4.16] Code and dataset release.
• [2024.3.31] Our tech report is available on arXiv.

🔨 Installation

Please refer to the following environmental settings that we use. You may install these packages by yourself if you meet any problem during automatic installation.

• CUDA 12.1
• FFmpeg 6.0
• Python 3.12.2
• PyTorch 2.2.1
• NNCore 0.4.2

Install from source

1. Clone the repository from GitHub.

git clone https://github.com/yeliudev/R2-Tuning.git
cd R2-Tuning

2. Initialize conda environment.

conda create -n r2-tuning python=3.12 -y
conda activate r2-tuning

3. Install dependencies.

pip install -r requirements.txt

🔖 Dataset

Option 1 [Recommended]: Download pre-extracted features from HuggingFace Hub directly.

# Prepare datasets in one command
bash tools/prepare_data.sh

Option 2: Reproduce our data pre-processing pipeline.

1. Download videos from the following links and place them into data/{dataset}/videos.

• QVHighlights
• Ego4D-NLQ
• Charades-STA
• TACoS
• YouTube Highlights
• TVSum

2. Extract and compress video frames at a fixed frame rate.

# For QVHighlights, Ego4D-NLQ, TACoS, and TVSum
python tools/extract_frames.py <path-to-videos>

# For Charades-STA
python tools/extract_frames.py <path-to-videos> --fps 1.0

# For YouTube Highlights
python tools/extract_frames.py <path-to-videos> --anno_path data/youtube/youtube_anno.json

• video_dir Path to the videos folder
• --anno_path Path to the annotation file (only for YouTube Highlights to compute frame rates)
• --frame_dir Path to the output extracted frames
• --size Side length of the cropped video frames
• --fps Frame rate to be used
• --max_len The maximum length of each video segment
• --workers Number of processes
• --chunksize The chunk size for each process

3. Extract features from video frames.

python tools/extract_feat.py <path-to-anno> <path-to-frames>

• anno_path Path to the annotation file
• frame_dir Path to the extracted frames
• --video_feat_dir Path to the output video features
• --query_feat_dir Path to the output query features
• --arch CLIP architecture to use (ViT-B/32, ViT-B/16, ViT-L/14, ViT-L/14-336px)
• --k Save the last k layers features
• --batch_size The batch size to use
• --workers Number of workers for data loader

The prepared dataset should be in the following structure.

R2-Tuning
├── configs
├── datasets
├── models
├── tools
├── data
│   ├── qvhighlights
│   │   ├── frames_224_0.5fps (optional)
│   │   ├── clip_b32_{vid,txt}_k4
│   │   └── qvhighlights_{train,val,test}.jsonl
│   ├── ego4d
│   │   ├── frames_224_0.5fps (optional)
│   │   ├── clip_b32_{vid,txt}_k4
│   │   └── nlq_{train,val}.jsonl
│   ├── charades
│   │   ├── frames_224_1.0fps (optional)
│   │   ├── clip_b32_{vid,txt}_k4
│   │   └── charades_{train,test}.jsonl
│   ├── tacos
│   │   ├── frames_224_0.5fps (optional)
│   │   ├── clip_b32_{vid,txt}_k4
│   │   └── {train,val,test}.jsonl
│   ├── youtube
│   │   ├── frames_224_auto (optional)
│   │   ├── clip_b32_{vid,txt}_k4
│   │   └── youtube_anno.json
│   └── tvsum
│       ├── frames_224_0.5fps (optional)
│       ├── clip_b32_{vid,txt}_k4
│       └── tvsum_anno.json
├── README.md
├── setup.cfg
└── ···

🔮 Training

Use the following commands to train a model with a specified config.

# Single GPU
python tools/launch.py <path-to-config>

# Multiple GPUs on a single node (elastic)
torchrun --nproc_per_node=<num-gpus> tools/launch.py <path-to-config>

# Multiple GPUs on multiple nodes (slurm)
srun <slurm-args> python tools/launch.py <path-to-config>

• config The config file to use
• --checkpoint The checkpoint file to load from
• --resume The checkpoint file to resume from
• --work_dir Working directory
• --eval Evaluation only
• --dump Dump inference outputs
• --seed The random seed to use
• --amp Whether to use automatic mixed precision training
• --debug Debug mode (detect nan during training)
• --launcher The job launcher to use

Please refer to the configs folder for detailed settings of each model.

🏆 Evaluation

Use the following command to test a model and evaluate results.

python tools/launch.py <path-to-config> --checkpoint <path-to-checkpoint> --eval

For QVHighlights, you may also dump inference outputs on val and test splits.

python tools/launch.py <path-to-config> --checkpoint <path-to-checkpoint> --dump

Then you can pack the hl_{val,test}_submission.jsonl files and submit them to CodaLab.

💻 Single Video Inference

[!WARNING] This feature is only compatible with nncore==0.4.4.

Use the following command to perform moment retrieval using your own videos and queries.

# Make sure you are using the correct version
pip install nncore==0.4.4

python tools/inference.py <path-to-video> <query> [--config <path-to-config> --checkpoint <path-to-checkpoint>]

The checkpoint trained on QVHighlights using this config will be downloaded by default.

📦 Model Zoo

We provide multiple pre-trained models and training logs here. All the models were trained on a single NVIDIA A100 80GB GPU and were evaluated using the default metrics of different datasets.

📖 Citation

Please kindly cite our paper if you find this project helpful.

@inproceedings{liu2024tuning,
  title={$R^2$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding},
  author={Liu, Ye and He, Jixuan and Li, Wanhua and Kim, Junsik and Wei, Donglai and Pfister, Hanspeter and Chen, Chang Wen},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2024}
}