LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Installation

See INSTALL.md for details on installing the codebase, including requirement and environment settings

Data

For data preparation and setup, our LSTC strictly follows the processing of PySlowFast, See DATASET.md for details on preparing the data.

Run the code

We take SlowFast-ResNet50 as an example

• train the model

python3 tools/run_net.py --cfg config/AVA/SLOWFAST_32x12_R50_LFB.yaml \
    AVA.FEATURE_BANK_PATH 'path/to/feature/bank/folder' \
    TRAIN.CHECKPOINT_FILE_PATH 'path/to/pretrained/backbone' \
    OUTPUT_DIR 'path/to/output/folder'

• test the model

python3 tools/run_net.py --cfg config/AVA/SLOWFAST_32x12_R50_LFB.yaml \
    AVA.FEATURE_BANK_PATH 'path/to/feature/bank/folder' \
    OUTPUT_DIR 'path/to/output/folder' \
    TRAIN.ENABLE False \ 
    TEST.ENABLE True

If you want to start the DDP training from command line with torch.distributed.launch, please set start_method='cmd' in tools/run_net.py

Resource

The codebase provide following resources for fast training and validation

Pretrained backbone on Kinetics

Extracted long term feature bank

Checkpoint file

Acknowledgement

This codebase is built upon PySlowFast.

Citation

If you find this repository helps your research, please refer following paper

@InProceedings{Yuxi_2021_ACM,
  author = {Li, Yuxi and Zhang, Boshen and Li, Jian and Wang, Yabiao and Wang, Chengjie and Li, Jilin and Huang, Feiyue and Lin, Weiyao},
  title = {LSTC: Boosting Atomic Action Detection with Long-Short-Term Context},
  booktitle = {ACM Conference on Multimedia},
  month = {October},
  year = {2021}
}