/SloshNet

[AAAI2023] Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition (SloshNet)

Primary LanguagePython

AAAI2023: Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition(SloshNet) [arXiv]

Overview

overview

Content

Prerequisites

The code is built with following libraries:

  • PyTorch >= 1.8
  • tensorboardX
  • pprint
  • tqdm
  • dotmap
  • yaml
  • csv

For video data pre-processing, you may need ffmpeg.

More detail information about libraries see INSTALL.md.

Data Preparation

We need to first extract videos into frames for fast reading. Please refer to TSN repo for the detailed guide of data pre-processing. We have successfully trained on Kinetics, SthV2 ,UCF101, HMDB51.

Training

We provided several examples to train SloshNet with this repo:

  • To train on Kinetics or SthV2 or Hmdb51 or UCF101 from Imagenet pretrained models, you can run:
# train Kinetics
 bash ./scripts/train_kin-1s.sh 

# train SthV2
 bash ./scripts/train_ssv2-1s.sh 

# train HMDB
bash ./scripts/train_hmdb-1s.sh 

# train UCF
bash ./scripts/train_ucf-1s.sh

Testing

To test the trained models, you can run scripts/run_test.sh. For example:

bash ./scripts/test.sh

Citing SloshNet

If you find SloshNet useful in your research, please cite our paper.

Acknowledgments

Our code is based on TRX.