MmCLIP

mmCLIP: Boosting mmWave-based Zero-shot HAR via Signal-Text Alignment

Implementation for the SenSys24 paper mmCLIP: Boosting mmWave-based Zero-shot HAR via Signal-Text Alignment.

Currently, we provide the deep learning model code and preprocessed synthetic dataset.

Requirements

conda create -n mmCLIP python=3.8.17

pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2

pip install matplotlib

pip install transformers==4.36.0

pip install git+https://github.com/openai/CLIP.git

pip install timm==0.9.12

pip install opencv-python

pip install pandas

pip install scikit-learn

Dataset Download

Please download the preprocessed synthetic signal dataset and LLM-augmented text descriptions from the following link:

https://purdue0-my.sharepoint.com/:f:/g/personal/cao393_purdue_edu/ElON7JfgsTRMuzxap8Kv6j4B4qeGC2qTndGjOvX5FJEdBw?e=ILTOFv

Pretrain with Synthetic Dataset

To pretrain model on synthetic dataset, run

python src/train_babel_gpt_v2.py

Fine-tune with Local data

Run Zero-shot mmCLIP with synthetic data pretraining:

python src/train_zs_real_finetune.py

Note that iteration 0 will be mmCLIP-Syn-Attr.

Run mmCLIP without synthetic data pretraining:

python src/train_zs_real_only.py

Run One-shot mmCLIP with synthetic data pretraining:

python src/train_zs_real_fewshot.py

If you find anything useful in our project, please consider citing our paper.

@inproceedings{cao2024mmclip,
  title={mmCLIP: Boosting mmWave-based Zero-shot HAR via Signal-Text Alignment},
  author={Cao, Qiming and Xue, Hongfei and Liu, Tianci and Wang, Xingchen and Wang, Haoyu and Zhang, Xincheng and Su, Lu},
  booktitle={Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems},
  pages={184--197},
  year={2024}
}