This is an official pytorch implementation of EZ-CLIP: Efficient Zero-Shot Video Action Recognition [arXiv]

Updates

Trained model download link of google driver.

Overview

Introduction

In this study, we present EZ-CLIP, a simple and efficient adaptation of CLIP that addresses these challenges. EZ-CLIP leverages temporal visual prompting for seamless temporal adaptation, requiring no fundamental alterations to the core CLIP architecture while preserving its remarkable generalization abilities. Moreover, we introduce a novel learning objective that guides the temporal visual prompts to focus on capturing motion, thereby enhancing its learning capabilities from video data.

Prerequisites

We provide the conda requirements.txt to help you install these libraries. You can initialize environment by using pip install -r requirements.txt.

Data Preparation

We need to first extract videos into frames for fast reading. Please refer 'Dataset_creation_scripts' data pre-processing. We have successfully trained on Kinetics, UCF101, HMDB51,

Training

# Train
python train.py --config configs/K-400/k400_train.yaml

Testing

# Test 
python test.py --config configs/ucf101/UCF_zero_shot_testing.yaml

Citation

If you find the code and pre-trained models useful for your research, please consider citing our paper:

@article{ez2022clip,
  title={EZ-CLIP: Efficient Zeroshot Video Action Recognition},
  author={Shahzad Ahmad, Sukalpa Chanda, Yogesh S Rawat},
  journal={arXiv preprint arXiv:2312.08010},
  year={2024}
}

Acknowledgments

Our code is based on ActionCLIP

Shahzadnit/EZ-CLIP