yuctian / actiondetection-dbg Goto Github PK

• 2020.03.13: Release tensorflow-version and pytorch-version DBG complete code.
• 2019.11.12: Release tensorflow-version DBG inference code.
• 2019.11.11: DBG is accepted by AAAI2020.
• 2019.11.08: Our ensemble DBG ranks No.1 on ActivityNet

In this repo, we propose a novel and unified action detection framework, named DBG, with superior performance over the state-of-the-art action detectors BSN and BMN. You can use the code to evaluate our DBG for action proposal generation or action detection. For more details, please refer to our paper Fast Learning of Temporal Action Proposal via Dense Boundary Generator!

• Paper Introduction
• Prerequisites
• Getting Started
• Citation
• Contact

This paper introduces a novel and unified temporal action proposal generator named Dense Boundary Generator (DBG). In this work, we propose dual stream BaseNet to generate two different level and more discriminative features. We then adopt a temporal boundary classification module to predict precise temporal boundaries, and an action-aware completeness regression module to provide reliable action completeness confidence.

• Tensorflow == 1.9.0 or PyTorch == 1.1
• Python == 3.6
• NVIDIA GPU == Tesla P40
• Linux CUDA 9.0 CuDNN
• gcc 5

Clone the github repository. We will call the cloned directory as $DBG_ROOT.

cd $DBG_ROOT

Firstly, you should compile our proposal feature generation layers.

Please compile according to the framework you need.

Compile tensorflow-version proposal feature generation layers:

cd tensorflow/custom_op
make

cd pytorch/custom_op
python setup.py install

# TensorFlow version (AUC result = 68.37%):
python tensorflow/test.py config/config_pretrained.yaml
python post_processing.py output/result/ results/result_proposals.json
python eval.py results/result_proposals.json

# PyTorch version (AUC result = 68.26%):
python pytorch/test.py config/config_pretrained.yaml
python post_processing.py output/result/ results/result_proposals.json
python eval.py results/result_proposals.json

# TensorFlow version:
python tensorflow/train.py config/config.yaml

# PyTorch version:
python pytorch/train.py config/config.yaml

# TensorFlow version:
python tensorflow/test.py config/config.yaml

# PyTorch version:
python pytorch/test.py config/config.yaml

python post_processing.py output/result/ results/result_proposals.json

python eval.py results/result_proposals.json

@inproceedings{DBG2020arXiv,
  author    = {Chuming Lin*, Jian Li*, Yabiao Wang, Ying Tai, Donghao Luo, Zhipeng Cui, Chengjie Wang, Jilin Li, Feiyue Huang, Rongrong Ji},
  title     = {Fast Learning of Temporal Action Proposal via Dense Boundary Generator},
  booktitle   = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
}

Jian Li: [email protected]
Chuming Lin: [email protected]