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SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders (ECCV 2024)

Sheng-Wei Li, Zi-Xiang Wei, Wei-Jie Chen, Yi-Hsin Yu, Chih-Yuan Yang†, Jane Yung-jen Hsu† (†corresponding authors)

What does "SA-DVAE" stand for?

SA-DVAE stands for Semantic Alignment via Disentangled Variational Autoencoders.

TL;DR

SA-DVAE improves zero-shot skeleton-based action recognition by aligning modality-specific VAEs and disentangling skeleton features into semantic and non-semantic parts, achieving better performance on NTU RGB+D, NTU RGB+D 120, and PKU-MMD datasets.

Setting Up the Environment

The codebase has been tested with the following setup:

• Operating System: Ubuntu 22.04
• Python Version: 3.10
• GPU: 1x NVIDIA RTX 3090 with CUDA version 12.7

1. Clone the Repository

   git clone https://github.com/pha123661/SA-DVAE.git
   cd SA-DVAE
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Download Pre-extracted Features

   • Download the pre-extracted features for the NTU-60, NTU-120, and PKU-MMD datasets here.
   • Extract the resources.zip file.
   • Place all subdirectories under ./resources.

   Optional: Generate Features Yourself

   • Download the class descriptions at ./class_lists and skeleton features at NTU RGB+D and PKUMMD.
   • Use sentence_transformers or transformers packages to extract semantic features.
   • Use mmaction2 to train and extract skeleton features.

4. Ensure that the directory structure is as follows:

   SA-DVAE
   ├── resources
   │   ├── label_splits
   │   ├── sk_feats
   │   └── text_feats
   ...
   

Training

We provide three training scripts in ./scripts, each corresponding to the three main experiments in our paper:

1. Comparison with SOTA Methods

   ./scripts/train_eval_synse_split.sh {dataset}
   

2. Random Class Splits

   ./scripts/train_eval_average_random_split.sh {dataset}
   

   • This script runs experiments on three different seen/unseen class splits.

3. Enhanced Class Descriptions by a Large Language Model (LLM)

   ./scripts/train_eval_llm_descriptions.sh {dataset}
   

   • This script runs experiments on three different seen/unseen class splits.

where dataset should be one of ntu60, ntu120, and pku51.

Training Steps

Each training script follows these four stages, covering both Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) training and evaluation:

1. Train and evaluate SA-DVAE for ZSL.

2. Prepare $\mathbf{p}_s$ and $\mathbf{p}_u$ for Domain Classifier Training.

3. Train the Domain Classifier.

4. Evaluate SA-DVAE under GZSL.

Acknowledgements

Our codebase is mainly built upon skelemoa/synse-zsl. We thank the authors for their excellent work.

Citation

@inproceedings{li2024sadvae,
  title={SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders},
  author={Sheng-Wei Li, Zi-Xiang Wei, Wei-Jie Chen, Yi-Hsin Yu, Chih-Yuan Yang, Jane Yung-jen Hsu},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024}
}