🚀 Large-scale Pre-training for Grounded Video Caption Generation

Evangelos Kazakos¹, Cordelia Schmid², Josef Sivic^1
1Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague
²Inria, École normale supérieure, CNRS, PSL Research University

📄 arXiv | 🌐 Project Website

📢 News

• 📚 09/11/2025: The HowToGround1M and iGround datasets are now available on 🤗 Hugging Face: HowToGround1M | iGround. They can be loaded directly with load_dataset() from the 🤗 Datasets library.
• 🤗 02/09/2025: We release grove-transformers — a lightweight, inference-only interface for GROVE, implemented with 🤗 Transformers.
• 💻 21/08/2025: Code, checkpoints, and datasets released!
• 🔥 25/06/2025: Paper accepted to ICCV 2025 🎉

---

📖 BibTeX

@inproceedings{kazakos2025grove,
  title     = {Large-scale Pre-training for Grounded Video Caption Generation},
  author    = {Evangelos Kazakos and Cordelia Schmid and Josef Sivic},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2025}
}

Quick start

If you only need inference for GROVE (quick experimentation, no training), use the lightweight grove-transformers package instead. Installation instructions are in its README and on the Hugging Face Hub.

If you want to train and/or evaluate GROVE end-to-end, follow the instructions below.

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Installation instructions

Create and activate a pip environment

First, create a new conda environment:

conda create -n grove python=3.12
conda activate grove

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Install PyTorch

Choose the CUDA version that matches your system (e.g., cu124, cu121, cu118).
Example for CUDA 12.4:

pip install --index-url https://download.pytorch.org/whl/cu124 \
    torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1
pip install torchtext==0.18.0 torchdata==0.9.0

💡 Replace cu124 in the URL with the correct CUDA version tag for your machine.

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Install mmdetection

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
git checkout cfd5d3a985b0249de009b67d04f37263e11cdf3d
pip install -e . --no-build-isolation
cd ..

Install mmcv

git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout 57c4e25e06e2d4f8a9357c84bcd24089a284dc88
pip install -r requirements/optional.txt
pip install -e . -v
cd ..

Install SAM2

git clone https://github.com/facebookresearch/sam2.git
cd sam2
git checkout 2b90b9f5ceec907a1c18123530e92e794ad901a4
pip install -e . --no-build-isolation
cd ..

Install Flash Attention

pip install flash-attn==2.7.3 --no-build-isolation

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Download Stanford CoreNLP

• Stanford CoreNLP 3.4.1 (for evaluation in HowToGround1M/iGround)

• Stanford CoreNLP 4.5.7 (for evaluation in ActivityNet-Entities)

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Install remaining dependencies

pip install -r requirements.txt

Data Preparation

HowToGround1M & iGround

1. Download HowTo100M videos (for pre-training on HowToGround1M)

   • Follow the instructions in the HowTo100M webpage
   • The webpage has some broken links and is currently under construction. The domain will change and the link above will be updated to the new domain

2. Download iGround videos (for fine-tuning/evaluating on iGround)

   • Fill in this form to obtain links to the iGround videos
   • Run the following script to download the iGround videos using the provided links

     bash scripts/download_iGround.sh iGround_links.txt /path/to/iground_videos_dir

   • Caution: the links expire in 7 days

3. Download annotations

   • HowToGround1M annotations
   • iGround annotations

4. Preprocess annotations

   • Run the following command to split the annotations into separate files per video:

     bash scripts/preprocess_howtoground_annot.py /path/to/{HowToGround1M,iGround}.pkl target_dir

Note: The iGround annotations include both processed and raw versions (e.g., iGround_train_set_processed.pkl vs iGround_train_set_raw.pkl). The processed annotations were used to train GROVE. Processing merges multiple instances of the same object type per frame into a single annotation by taking the union of all bounding boxes for that instance. The raw annotations are unprocessed — the same object type may appear multiple times in a frame, each with its own distinct bounding box per instance.

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ActivityNet-Entities

1. Download ActivityNet videos

   • From Hugging Face

2. Download annotations

   • ActivityNet-Entities annotations

3. Preprocess videos

   bash scripts/preprocess_anet_videos.sh input_dataset_dir preprocessed_dataset_dir

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VidSTG

1. Download VidSTG videos

   • From Hugging Face

2. Download annotations

   • VidSTG annotations

Checkpoints

• Download GROVE pre-trained on HowToGround1M from link
• Download GROVE fine-tuned on iGround from link
• Download SAM checkpoint from link
• Run:

  mkdir checkpoints
  mv /path/to/checkpoints checkpoints/

Training (using SLURM's sbatch)

• In train_scripts/train_{howtoground,vidstg,anet}.sh:
  1. (Optional) Modify the sbatch configuration based on your cluster's configuration, though it is suggested to use the provided ones
  2. Modify the path to the data and checkpoint
• Run:

  bash train_scripts/train_{howtoground,vidstg,anet}.sh

Note: train_scripts/train_howtoground.sh can be used for both HowToGround1M and iGround datasets.

Inference & evaluation

Below, it is shown how to run inference & evaluation on iGround validation and test sets. Similarly, for the other datasets use the scripts found in infer_eval_scripts/

• For iGround validation set:

  bash infer_eval_scripts/infer_eval_iground.sh checkpoints/grove_ft_iground_ckpt.bin /path/to/save/token_embedings.pt /path/to/save/preds.pkl /path/to/iGround_val_set_raw.pkl /path/to/iground_videos_dir 0.5 /path/to/stanford-corenlp-full-2014-08-27

• For iGround test set:

  bash infer_eval_scripts/infer_eval_iground.sh checkpoints/grove_ft_iground_ckpt.bin /path/to/save/token_embedings.pt /path/to/save/preds.pkl /path/to/iGround_test_set_raw.pkl /path/to/iground_videos_dir 0.5 /path/to/stanford-corenlp-full-2014-08-27

Note: By downloading Stanford CoreNLP from the links provided in the installation instructions, you will get a directory stanford-corenlp-full-2014-08-27 which contains Stanford CoreNLP 3.4.1 (used above for evaluation in iGround) and a directory stanford-corenlp-4.5.7 which contains Stanford CoreNLP 4.5.7 (used for evaluation in ActivityNet-Entities).