You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Convolutional Two-Stream Network Fusion for Video Action Recognition
This repository contains the code for our CVPR 2016 paper:
Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
"Convolutional Two-Stream Network Fusion for Video Action Recognition"
in Proc. CVPR 2016
If you find the code useful for your research, please cite our paper:
@inproceedings{feichtenhofer2016convolutional,
title={Convolutional Two-Stream Network Fusion for Video Action Recognition},
author={Feichtenhofer, Christoph and Pinz, Axel and Zisserman, Andrew},
booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}
}
Requirements
The code was tested on Ubuntu 14.04 and Windows 10 using MATLAB R2015b and
NVIDIA Titan X or Z GPUs.
If you have questions regarding the implementation please contact:
Christoph Feichtenhofer <feichtenhofer AT tugraz.at>
Download the code git clone --recursive https://github.com/feichtenhofer/twostreamfusion
Compile the code by running compile.m.
This will also compile a modified (and older) version of the
MatConvNet toolbox. In case of any issues,
please follow the installation instructions on the
MatConvNet homepage.
Edit the file cnn_setup_environment.m to adjust the models and data paths.
Download pretrained model files and the datasets, linked below and unpack them into your models/data directory.
Optionally you can pretrain your own twostream models by running
cnn_ucf101_spatial(); to train the appearance network stream.
cnn_ucf101_temporal(); to train the optical flow network stream.
Run
cnn_ucf101_fusion(); this will use the downloaded models and demonstrate training of our final architecture on UCF101/HMDB51.
In case you would like to train on the CPU, clear the variable opts.train.gpus
In case you encounter memory issues on your GPU, consider decreasing the cudnnWorkspaceLimit (512MB is default)
Pretrained models
Download our baseline networks trained on UCF101 here: