Skip to content

Latest commit

 

History

History
82 lines (66 loc) · 2.21 KB

README.md

File metadata and controls

82 lines (66 loc) · 2.21 KB
Raw

LongVLM: Efficient Long Video Understanding via Large Language Models

[ECCV 2024] This is the official repository for our paper: LongVLM: Efficient Long Video Understanding via Large Language Models by Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang and Bohan Zhuang.

Architecture

main

Installation

conda create -n longvlm python==3.9
pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1
pip install -r requirements.txt
export PYTHONPATH="./:$PYTHONPATH"

### For Training, install flash-attention
pip install ninja
git clone https://github.com/HazyResearch/flash-attention.git
cd flash-attention
git checkout v1.0.7
python setup.py install

Pretrained model

Download pretrained base model and delta weights from Huggingface.

Apply delta weights to the base model:

python scripts/apply_delta.py \
    --base YOUR_PATH_TO_LLAMA_7B \
    --target YOUR_PATH_TO_LLAVA_7B \
    --delta YOUR_PATH_TO_LLAVA_DELTA_7B

Data preparation

Download ActivityNet dataset. Dataset architecture:

datasets
|___anet
|   |___v1-2/
|   |   |___train/
|   |   |   |___xxxx.mp4
|   |   |   |___xxxx.mp4
|   |   |___val/
|   |   |   |___xxxx.mp4
|   |   |   |___xxxx.mp4
|   |___v1-3/
|   |   |___train_val/
|   |   |   |___xxxx.mp4
|   |   |   |___xxxx.mp4
|   |___anet_benchmark_video_id.txt
|   |___video_list_v1_2_val.txt
|   |___video_list_v1_2_train.txt
|   |___video_list_v1_3_trainval.txt

Extract visual features using CLIP-VIT/L-14:

python scripts/save_features.py \
    --video_dir_path YOUR_PATH_TO_VIDEO_FOLDER \
    --list_file YOUR_PATH_TO_VIDEO_ID_FILE \
    --clip_feat_path_local YOUR_PATH_TO_SAVE_VISUAL_FEATURE_L \
    --clip_feat_path_mem YOUR_PATH_TO_SAVE_VISUAL_FEATURE_G

Train and Evaluation

sh run.sh

Examples

main main main main

Acknowledgement

This work is built upon VideoChatGPT. Thanks to their awesome work.