This repo contains code for our CVPR2025 paper VISTA, a video spatiotemporal augmentation method that generates long-duration and high-resolution video instruction-following data to enhance the video understanding capabilities of video LMMs.

🌐 Homepage | 📖 arXiv | 💻 GitHub | 🤗 VISTA-400K | 🤗 Models | 🤗 HRVideoBench

• [2025-02-26]: Our paper has been accepted by CVPR 2025!
• [2025-01-07]: Release code for model training and evaluation.
• [2024-12-22]: Our models, datasets and HRVideoBench are now available at 🤗 Hugging Face.

Please use the following commands to install the required packages:

conda env create -f environment.yaml
conda activate vista
pip install flash-attn --no-build-isolation

1. Modify the data configuration files under train/data_configs/ to point to the correct paths of VISTA subsets.
2. Follow the commands below to train a VISTA model:

bash scripts/idefics2/train_idefics2.sh
bash scripts/longva/train_longva.sh
bash scripts/videollava/train_videollava.sh

Use the scripts under eval/ to evaluate VISTA models. For example, to evaluate Video-MME for VISTA-LongVA, use the command:

python eval_video_mme.py --model_type longva --model_name_or_path TIGER-Lab/VISTA-LongVA --num_frames 64 --data_dir <path_to_videomme_data>

VISTA leverages insights from image and video classification data augmentation techniques such as CutMix, MixUp and VideoMix, which demonstrate that training on synthetic data created by overlaying or mixing multiple images or videos results in more robust classifiers. Similarly, our method spatially and temporally combines videos to create (artificial) augmented video samples with longer durations and higher resolutions, followed by synthesizing instruction data based on these new videos. Our data synthesis pipeline utilizes existing public video-caption datasets, making it fully open-sourced and scalable. This allows us to construct VISTA-400K, a high-quality video instruction-following dataset aimed at improving the long and high-resolution video understanding capabilities of video LMMs.

If you find our paper useful, please cite us with

@misc{ren2024vistaenhancinglongdurationhighresolution,
      title={VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation}, 
      author={Weiming Ren and Huan Yang and Jie Min and Cong Wei and Wenhu Chen},
      year={2024},
      eprint={2412.00927},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.00927}, 
}