Awesome

ViLA: Efficient Video-Language Alignment for Video Question Answering [ECCV2024]

In this work, we propose an efficient Video-Language Alignment (ViLA) network. Our ViLA model addresses both efficient frame sampling and effective cross-modal alignment in a unified way. In our ViLA network, we design a new learnable text-guided Frame-Prompter together with a new cross-modal distillation (QFormer-Distiller) module. Pre-trained large image-language models have shown promising results on problems such as visual question answering (VQA). However, how to efficiently and effectively sample video frames when adapting pre-trained large image-language model to video-language alignment is still the major challenge. Compared with prior work, our ViLA model demonstrates the capability of selecting key frames with critical contents, thus improving the video-language alignment accuracy while reducing the inference latency +3.3% on NExT-QA Temporal with 3.0X speed up). Overall, our ViLA network outperforms the state-of-the-art methods on the video question-answering benchmarks: +4.6% on STAR Interaction, +2.2% on STAR average with 3.0X speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with 4.2X speed-up.

Code structure

# data & data preprocessing
./vila_data

# pretrained checkpoints
./vila_checkpoints


# ViLA code
./lavis/models/vila_models/


# running scripts for ViLA training
./run_scripts

Setup

Install Dependencies

1. (Optional) Creating conda environment

conda create -n vila python=3.8
conda activate vila

2. build from source

pip install -e .

Dataset Preparation

We test our model on:

• NExT-QA

• STAR

• How2QA

• TVQA

• VLEP

• QVHighlights

Please download original QA data and preprocess them via our scripts.

Training

We provide VLAP training script examples as follows.

And please change your data path.

1) Pre-training teacher

sh run_scripts/vila/finetune/star.sh
sh run_scripts/vila/finetune/star_8f.sh
sh run_scripts/vila/finetune/star_f32_f16.sh

2) prepare weight (change the model path first)

python re_weight.py

3) Training

sh run_scripts/vila/finetune/star_vila_32t4f_dist_decode.sh

3) Training with LoRA

Check ./lavis/models/vila_models/vila_lora.py

Acknowledgments

We thank the developers of SeViLA, LAVIS, BLIP-2, CLIP, All-in-One, for their public code release.

Citing Swin-MoE

@misc{wang2024vilaefficientvideolanguagealignment,
      title={ViLA: Efficient Video-Language Alignment for Video Question Answering},
      author={Xijun Wang and Junbang Liang and Chun-Kai Wang and Kenan Deng and Yu Lou and Ming Lin and Shan Yang},
      year={2024},
      eprint={2312.08367},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2312.08367},
}

License

This project is licensed under the Apache-2.0 License.