Awesome

3C-Net

Overview

This package is a PyTorch implementation of our paper 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization, to be published at ICCV 2019.

Data

We use the same I3D features, for the Thumos14 and ActivityNet 1.2 datasets, released by Sujoy Paul. The annotations are already included in this repository.

Training 3C-Net

The model can be trained using the following commands. See options.py for additional parse arguments

# Running on Thumos14 
python main.py --dataset-name Thumos14
# Running on ActivityNet 1.2
python main.py --dataset-name ActivityNet1.2 --activity-net --num-class 100

Citation

Please cite the following work if you use this package.

@inproceedings{narayan20193c,
  title={3c-net: Category count and center loss for weakly-supervised action localization},
  author={Narayan, Sanath and Cholakkal, Hisham and Khan, Fahad Shahbaz and Shao, Ling},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2019}
}

Dependencies

This codebase was built on the W-TALC package found here and has the following dependencies.

PyTorch 0.4.1, Tensorboard Logger 0.1.0
Python 3.6
numpy, scipy among others