{reza.s,zhang.yuex,mohsen,o.camps}@northeastern.edu
- Aug 22, 2024 - EGTEA pre-extracted features and config files for other datasets added
- Aug 14, 2024 - Arxiv preprint added
- July 7, 2024 - initial code release
- Ubuntu 20.04
- Python 3.10.9
- CUDA 12.0
- pytorch==2.0.0
- numpy==1.23.5
- h5py==3.9.0
- ...
To install all required libraries, execute the pip command below.
pip install -r requirement.txt
The Kinetics I3D pre-trained feature of EGTEA dataset can be downloaded from GDrive link.
Files should be located in 'data/'.
You can get other features from the following links -
The configuration files for EGTEA are already provided in the repository. For other datasets, they can be downloaded from GDrive link.
To train the main HAT model, execute the command below.
python main.py --mode=train --split=[split #]*
*If the dataset has any splits (e.g., EGTEA has 4 splits)
To train the post-processing network (OSN), execute the commands below.
python supnet.py --mode=make --inference_subset=train --split=[split #]
python supnet.py --mode=make --inference_subset=test --split=[split #]
python supnet.py --mode=train --split=[split #]
To test HAT, execute the command below.
python main.py --mode=test --split=[split #]
Please cite our paper in your publications if it helps your research:
@inproceedings{reza2022history,
title={HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization},
author={Reza, Sakib and Zhang, Yuexi and Moghaddam, Mohsen and Camps, Octavia},
booktitle={European Conference on Computer Vision},
pages={XXX--XXX},
year={2024},
organization={Springer}
}This repository is created based on the repository of the baseline work OAT-OSN.