Awesome

Prompting Visual-Language Models for Efficient Video Understanding

Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, Weidi Xie. 　ECCV 2022.

[project page] [Arxiv] [Video]

Get Started on HMDB51 (More datasets will be available soon)

Environment

• python >= 3.6.10
• pytorch >= 1.7.1
• tensorboardX
• einops
• tqdm

Data Preparation

• Download the CLIP pre-trained features of HMDB51 from here.

  Unzip the features, and put them under the ./feat folder.

• Download the pre-train model of HMDB51 from here, put it under the ./models folder.

  After the preparation work, the whole project should have the following structure:

  This folder
  ├── README.md
  │   ...     
  │                                
  ├── feat                                                     
  │   └── HMDB
  │       ├── #2_Gum_chew_h_nm_np1_fr_med_0.npy
  │       ├── #2_Gum_chew_h_nm_np1_fr_med_1.npy
  │       |   ...  
  │  
  ├── models                                    
  │   └── HMDB_best.pth.tar
  │   
  │  ... 
  

Training

cd ./src
python main.py -j 8 --prefix 16 --postfix 16 --tfm_layers 1 --tfm_heads 8 --dataset HMDB51-feature-30fps-center --temporal 1 --batchsize 64 --lr 1e-4 --featnorm 1 --verbose Temp --num_iterations 1100 --save_iterations 55

Evaluation

cd ./src
python main.py -j 8 --prefix 16 --postfix 16 --tfm_layers 1 --tfm_heads 8 --dataset HMDB51-feature-30fps-center --temporal 1 --batchsize 64 --lr 1e-4 --featnorm 1 --verbose Temp --test path_to_checkpoint

[Optional] Evaluating with Our Pre-trained Model

cd ./src
python main.py -j 8 --prefix 16 --postfix 16 --tfm_layers 1 --tfm_heads 8 --dataset HMDB51-feature-30fps-center --temporal 1 --batchsize 64 --lr 1e-4 --featnorm 1 --verbose Temp --test ../models/HMDB_best.pth.tar

Reference

@inproceedings{ju2022prompting,
  title={Prompting Visual-Language Models for Efficient Video Understanding}
  author={Chen Ju and Tengda Han and Kunhao Zheng and Ya Zhang and Weidi Xie},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2022}
}