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GAP

This repo is the official implementation for ICCV23 paper "GAP: Generative Action Description Prompts for Skeleton-based Action Recognition" previously known as "LST: Language Supervised Training for Skeleton-based Action Recognition" (arxiv version)

Introduction

Skeleton-based action recognition has recently received considerable attention. Current approaches to skeleton-based action recognition are typically formulated as one-hot classification tasks and do not fully exploit the semantic relations between actions. For example, "make victory sign" and "thumb up" are two actions of hand gestures, whose major difference lies in the movement of hands. This information is agnostic from the categorical one-hot encoding of action classes but could be unveiled from the action description. Therefore, utilizing action description in training could potentially benefit representation learning. In this work, we propose a Generative Action-description Prompts (GAP) approach for skeleton-based action recognition. More specifically, we employ a pre-trained large-scale language model as the knowledge engine to automatically generate text descriptions for body parts movements of actions, and propose a multi-modal training scheme by utilizing the text encoder to generate feature vectors for different body parts and supervise the skeleton encoder for action representation learning. Experiments show that our proposed GAP method achieves noticeable improvements over various baseline models without extra computation cost at inference. GAP achieves new state-of-the-arts on popular skeleton-based action recognition benchmarks, including NTU RGB+D, NTU RGB+D 120 and NW-UCLA.

Architecture of GAP

Prerequisites

• Python >= 3.6

• PyTorch >= 1.1.0

• PyYAML, tqdm, tensorboardX

• We provide the dependency file of our experimental environment, you can install all dependencies by creating a new anaconda virtual environment and running pip install -r requirements.txt

• Run pip install -e torchlight

Data Preparation

Please follow CTR-GCN for data preparation

Download datasets.

There are 3 datasets to download:

• NTU RGB+D 60 Skeleton
• NTU RGB+D 120 Skeleton
• NW-UCLA

NTU RGB+D 60 and 120

1. Request dataset here: https://rose1.ntu.edu.sg/dataset/actionRecognition
2. Download the skeleton-only datasets:
   1. nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
   2. nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120)
   3. Extract above files to ./data/nturgbd_raw

NW-UCLA

1. Download dataset from here
2. Move all_sqe to ./data/NW-UCLA

Data Processing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - NW-UCLA/
    - all_sqe
      ... # raw data of NW-UCLA
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

Generating Data

• Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:

 cd ./data/ntu # or cd ./data/ntu120
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python seq_transformation.py

Training & Testing

Training

• To train model on NTU60/120

# Example: training GAP on NTU RGB+D cross subject joint modality
CUDA_VISIBLE_DEVICES=0,1 python main_multipart_ntu.py --config config/nturgbd-cross-subject/lst_joint.yaml --model model.ctrgcn.Model_lst_4part --work-dir work_dir/ntu60/csub/lst_joint --device 0 1
# Example: training GAP on NTU RGB+D cross subject bone modality
CUDA_VISIBLE_DEVICES=0,1 python main_multipart_ntu.py --config config/nturgbd-cross-subject/lst_bone.yaml --model model.ctrgcn.Model_lst_4part_bone --work-dir work_dir/ntu60/csub/lst_bone --device 0 1
# Example: training GAP on NTU RGB+D 120 cross subject joint modality
CUDA_VISIBLE_DEVICES=0,1 python main_multipart_ntu.py --config config/nturgbd120-cross-subject/lst_joint.yaml --model model.ctrgcn.Model_lst_4part --work-dir work_dir/ntu120/csub/lst_joint --device 0 1
# Example: training GAP on NTU RGB+D 120 cross subject bone modality
CUDA_VISIBLE_DEVICES=0,1 python main_multipart_ntu.py --config config/nturgbd120-cross-subject/lst_bone.yaml --model model.ctrgcn.Model_lst_4part_bone --work-dir work_dir/ntu120/csub/lst_bone --device 0 1

• To train model on NW-UCLA

CUDA_VISIBLE_DEVICES=0,1 python main_multipart_ucla.py --config config/ucla/lst_joint.yaml --model model.ctrgcn.Model_lst_4part_ucla --work-dir work_dir/ucla/lst_joint --device 0 1

Testing

• To test the trained models saved in <work_dir>, run the following command:

python main_multipart_ntu.py --config <work_dir>/config.yaml --work-dir <work_dir> --phase test --save-score True --weights <work_dir>/xxx.pt --device 0

• To ensemble the results of different modalities, run

# Example: ensemble four modalities of GAP on NTU RGB+D 120 cross subject
python ensemble.py --datasets ntu120/xsub --joint-dir work_dir/ntu120/csub/lst_joint --bone-dir work_dir/ntu120/csub/lst_bone --joint-motion-dir work_dir/ntu120/csub/lst_joint_vel --bone-motion-dir work_dir/ntu120/csub/lst_bone_vel

Acknowledgements

This repo is based on CTR-GCN. The data processing is borrowed from SGN and HCN. The code for different modality is adopted from InfoGCN. The implementation for contrastive loss is adopted from ActionCLIP.

Thanks to the original authors for their work!

Citation

Please cite this work if you find it useful:

@inproceedings{xiang2023gap,
    title={Generative Action Description Prompts for Skeleton-based Action Recognition},
    author={Wangmeng Xiang, Chao Li, Yuxuan Zhou, Biao Wang, Lei Zhang},
    booktitle={ICCV},
    year={2023}
}

@article{xiang2022lst,
    title={Language Supervised Training for Skeleton-based Action Recognition},
    author={Wangmeng Xiang, Chao Li, Yuxuan Zhou, Biao Wang, Lei Zhang},
    journal={arXiv preprint arXiv:2208.05318},
    year={2022}
}