Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, Weidi Xie. ECCV 2022.
[project page] [Arxiv] [Video]
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
│
│ ...
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
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
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
@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}
}