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This is an official pytorch implementation of EZ-CLIP: Efficient Zero-Shot Video Action Recognition [arXiv]UpdatesTrained model download link of google driver.OverviewIntroductionIn this study, we present EZ-CLIP, a simple and efficient adaptation of CLIP thataddresses these challenges. EZ-CLIP leverages temporal visual prompting forseamless temporal adaptation, requiring no fundamental alterations to the coreCLIP architecture while preserving its remarkable generalization abilities. Moreover, we introduce a novel learning objective that guides the temporal visualprompts to focus on capturing motion, thereby enhancing its learning capabilities from video data.ContentPrerequisitesModel ZooData PreparationTrainingTestingAcknowledgmentsPrerequisitesWe provide the conda requirements.txt to help you install these libraries. You can initialize environment by using pip install -r requirements.txt.Model ZooNOTE: All models in our experiments below uses publicly available ViT/B-16 based CLIP model.Zero-shot resultsAll models are trained on Kinetics-400 and then evaluated directly on downstream datasets.ModelInputHMDB-51UCF-101Kinetics-600ModelEZ-CLIP(ViT-16)8x22452.979.170.1linkBase-to-novel generalization resultsHere, we divide each dataset into base and novel classes.All models are trained on base classes and evaluated on both base and novel classes.DatasetInputBase Acc.Novel Acc.HMModelK-4008x22473.160.666.3linkHMDB-518x22477.058.266.3linkUCF-1018x22494.477.985.4linkSSV28x22416.613.314.8LinkData PreparationWe 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# Trainpython train.py --config configs/K-400/k400_train.yamlTesting# Test python test.py --config configs/ucf101/UCF_zero_shot_testing.yamlCitationIf 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}}AcknowledgmentsOur code is based on ActionCLIP

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