This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv]

Overview

ActionCLIP

Content

Prerequisites

The code is built with following libraries:

  • PyTorch >= 1.8
  • wandb
  • RandAugment
  • 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, UCF101, HMDB51, Charades.

Updates

  • We now support single crop validation(including zero-shot) on Kinetics-400, UCF101 and HMDB51. The pretrained models see MODEL_ZOO.md for more information.
  • we now support the model-training on Kinetics-400, UCF101 and HMDB51 on 8, 16 and 32 frames. The model-training configs see configs/README.md for more information.
  • We now support the model-training on your own datasets. The detail information see configs/README.md.

Pretrained Models

Training video models is computationally expensive. Here we provide some of the pretrained models. We provide a large set of trained models in the ActionCLIP MODEL_ZOO.md.

Kinetics-400

We experiment ActionCLIP with different backbones(we choose Transf as our final visual prompt since it obtains the best results) and input frames configurations on k400. Here is a list of pre-trained models that we provide (see Table 6 of the paper).

model n-frame top1 Acc(single-crop) top5 Acc(single-crop) checkpoint
ViT-B/32 8 78.36% 94.25% link pwd:8hg2
ViT-B/16 8 81.09% 95.49% link
ViT-B/16 16 81.68% 95.87% link
ViT-B/16 32 82.32% 96.20% link pwd:v7nn

HMDB51 && UCF101

On HMDB51 and UCF101 datasets, the accuracy(k400 pretrained) is reported under the accurate setting.

HMDB51

model n-frame top1 Acc(single-crop) checkpoint
ViT-B/16 32 76.2% link

UCF101

model n-frame top1 Acc(single-crop) checkpoint
ViT-B/16 32 97.1% link

Testing

To test the downloaded pretrained models on Kinetics or HMDB51 or UCF101, you can run scripts/run_test.sh. For example:

# test
bash scripts/run_test.sh  ./configs/k400/k400_ft_tem.yaml

Zero-shot

We provide several examples to do zero-shot validation on kinetics-400, UCF101 and HMDB51.

  • To do zero-shot validation on Kinetics from CLIP pretrained models, you can run:

# zero-shot
bash scripts/run_test.sh  ./configs/k400/k400_ft_zero_shot.yaml
  • To do zero-shot validation on UCF101 and HMDB51 from Kinetics pretrained models, you need first prepare the k400 pretrained model and then you can run:

# zero-shot
bash scripts/run_test.sh  ./configs/hmdb51/hmdb_ft_zero_shot.yaml

Training

We provided several examples to train ActionCLIP with this repo:

  • To train on Kinetics from CLIP pretrained models, you can run:

# train 
bash scripts/run_train.sh  ./configs/k400/k400_ft_tem_test.yaml
  • To train on HMDB51 from Kinetics400 pretrained models, you can run:

# train 
bash scripts/run_train.sh  ./configs/hmdb51/hmdb_ft.yaml
  • To train on UCF101 from Kinetics400 pretrained models, you can run:

# train 
bash scripts/run_train.sh  ./configs/ucf101/ucf_ft.yaml

More training details, you can find in configs/README.md

Contributors

ActionCLIP is written and maintained by Mengmeng Wang and Jiazheng Xing.

Citing ActionCLIP

If you find ActionClip useful in your research, please use the following BibTex entry for citation.

@inproceedings{wang2022ActionCLIP,
  title={ActionCLIP: A New Paradigm for Video Action Recognition},
  author={Mengmeng Wang, Jiazheng Xing and Yong Liu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2021}
} 

Acknowledgments

Our code is based on and STM.

GitHub

https://github.com/sallymmx/ActionCLIP