This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

Overview

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 CLIP and STM.

Blender add-on: Add to Cameras menu: View → Camera, View → Add Camera, Camera → View, Previous Camera, Next Camera

Blender add-on: Camera additions In 3D view, it adds these actions to the View|Cameras menu: View → Camera : set the current camera to the 3D view Vie

German Bauer 11 Feb 08, 2022
Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

Order of the Overflow 46 Sep 13, 2022
Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Codes-for-Algorithms Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Tracy (Shengmin) Tao 1 Apr 12, 2022
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation (CVPR2019) This is a pytorch implementatio

Yawei Luo 280 Jan 01, 2023
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
This repo contains code to reproduce all experiments in Equivariant Neural Rendering

Equivariant Neural Rendering This repo contains code to reproduce all experiments in Equivariant Neural Rendering by E. Dupont, M. A. Bautista, A. Col

Apple 83 Nov 16, 2022
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

Jianquan Ye 298 Dec 21, 2022
High-resolution networks and Segmentation Transformer for Semantic Segmentation

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

HRNet 2.8k Jan 07, 2023
2021搜狐校园文本匹配算法大赛 分比我们低的都是帅哥队

sohu_text_matching 2021搜狐校园文本匹配算法大赛Top2:分比我们低的都是帅哥队 本repo包含了本次大赛决赛环节提交的代码文件及答辩PPT,提交的模型文件可在百度网盘获取(链接:https://pan.baidu.com/s/1T9FtwiGFZhuC8qqwXKZSNA ,

hflserdaniel 43 Oct 01, 2022
Code for ECIR'20 paper Diagnosing BERT with Retrieval Heuristics

Bert Axioms This is the repository with the code for the Paper Diagnosing BERT with Retrieval Heuristics Required Data In order to run this code, you

Arthur Câmara 5 Jan 21, 2022
Citation Intent Classification in scientific papers using the Scicite dataset an Pytorch

Citation Intent Classification Table of Contents About the Project Built With Installation Usage Acknowledgments About The Project Citation Intent Cla

Federico Nocentini 4 Mar 04, 2022
Time series annotation library.

CrowdCurio Time Series Annotator Library The CrowdCurio Time Series Annotation Library implements classification tasks for time series. Features Suppo

CrowdCurio 51 Sep 15, 2022
Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

Conditional Smiles! (SmileCVAE) About Implementation of AE, VAE and CVAE. Trained CVAE on faces from UTKFace Dataset. Using an encoding of the Smile-s

Raúl Ortega 3 Jan 09, 2022
Pytorch implementation of Zero-DCE++

Zero-DCE++ You can find more details here: https://li-chongyi.github.io/Proj_Zero-DCE++.html. You can find the details of our CVPR version: https://li

Chongyi Li 157 Dec 23, 2022
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang

Pu Ren 11 Aug 23, 2022
An implementation of a discriminant function over a normal distribution to help classify datasets.

CS4044D Machine Learning Assignment 1 By Dev Sony, B180297CS The question, report and source code can be found here. Github Repo Solution 1 Based on t

Dev Sony 6 Nov 09, 2021
A simple code to perform canny edge contrast detection on images.

CECED-Canny-Edge-Contrast-Enhanced-Detection A simple code to perform canny edge contrast detection on images. A simple code to process images using c

Happy N. Monday 3 Feb 15, 2022
Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

Damir Temir 51 Dec 29, 2022
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

AutoML-Freiburg-Hannover 57 Nov 30, 2022
[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

331 Dec 28, 2022