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$\boldsymbol{R^2}$-Tuning

arXiv License Hugging Face Spaces

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
  1. Initialize conda environment.
conda create -n r2-tuning python=3.12 -y
conda activate r2-tuning
  1. 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.
  1. 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
Arguments of tools/extract_frames.py
  • 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
  1. Extract features from video frames.
python tools/extract_feat.py <path-to-anno> <path-to-frames>
Arguments of tools/extract_feat.py
  • 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>
Arguments of tools/launch.py
  • 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.

Dataset Config R1@0.3 R1@0.5 R1@0.7 MR mAP HD mAP Download
QVHighlights Default 78.71 67.74 51.87 47.86 39.45 model | log
Ego4D-NLQ Default 7.18 4.54 2.25 — — model | log
Charades-STA Default 70.91 60.48 38.66 — — model | log
TACoS Default 50.96 40.69 25.69 — — model | log
YouTube
Highlights 		Dog — — — — 74.26 model | log
Gymnastics — — — — 72.07 model | log
Parkour — — — — 81.02 model | log
Skating — — — — 76.26 model | log
Skiing — — — — 74.36 model | log
Surfing — — — — 82.76 model | log
TVSum BK — — — — 91.23 model | log
BT — — — — 92.35 model | log
DS — — — — 80.88 model | log
FM — — — — 75.61 model | log
GA — — — — 89.51 model | log
MS — — — — 85.01 model | log
PK — — — — 82.82 model | log
PR — — — — 90.39 model | log
VT — — — — 89.81 model | log
VU — — — — 85.90 model | log

📖 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}
}