CLAMP

By Xu Zhang, Wen Wang, Zhe Chen, Yufei Xu, Jing Zhang, and Dacheng Tao

This repository is an official implementation of CLAMP in the paper CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose, which is accepted to CVPR 2023.

Main Results

Models can be downloaded from Google Drive

Installation

Requirements

• Linux, CUDA>=9.2, GCC>=5.4

• Python>=3.6

• PyTorch>=1.5.0, torchvision>=0.6.0 (following instructions here)

• mmcv

  cd mmcv
  pip install -r requirements.txt
  pip install -v -e .
  

• mmpose

  cd ..
  pip install -r requirements.txt
  pip install -v -e .
  

Usage

Dataset preparation

Please download the dataset from AP-10K.

CLIP-pretrained models

Please download CLIP pretrained models from CLIP.

Training

Training CLAMP on AP-10K

bash tools/dist_train.sh configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/CLAMP_ViTB_ap10k_256x256.py 4 "0,1,2,3"

Evaluation

You can get the pretrained model (the link is in "Main Results" session), then run following command to evaluate it on the validation set:

bash tools/dist_test.sh configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/CLAMP_ViTB_ap10k_256x256.py work_dirs/CLAMP_ViTB_ap10k_256x256/epoch_210.pth 4 "0,1,2,3"

Acknowledgement

This project is based on mmpose, AP-10K, CLIP, and DenseCLIP. Thanks for their wonderful works. See LICENSE for more details.

Citing CLAMP

If you find CLAMP useful in your research, please consider citing:

@inproceedings{zhang2023clamp,
  title={CLAMP: Prompt-Based Contrastive Learning for Connecting Language and Animal Pose},
  author={Zhang, Xu and Wang, Wen and Chen, Zhe and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={23272--23281},
  year={2023}
}