Learning Video Representations from Correspondence Proposals
Created by Xingyu Liu, Joon-Young Lee and Hailin Jin from Stanford University and Adobe Research (paper link).
Citation
If you find our work useful in your research, please cite:
@article{liu:2019:cpnet,
title={Learning Video Representations from Correspondence Proposals},
author={Xingyu Liu and Joon-Young Lee and Hailin Jin},
journal={CVPR},
year={2019}
}
Abstract
Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular structure and complex dynamics. In this paper, we propose a novel neural network that learns video representations by aggregating information from potential correspondences. This network, named CPNet, can learn evolving 2D fields with temporal consistency. In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input. We provide extensive ablation experiments to validate our model. CPNet shows stronger performance than existing methods on Kinetics and achieves the state-of-the-art performance on Something-Something and Jester. We provide analysis towards the behavior of our model and show its robustness to errors in proposals.
Installation
Install TensorFlow. The code is tested under TF1.9.0 GPU version, g++ 5.4.0, CUDA 9.0 and Python 3.5 on Ubuntu 16.04. There are also some dependencies for a few Python libraries for data processing and visualizations like cv2. It's highly recommended that you have access to GPUs.
Compile Customized TF Operators
The TF operators are included under tf_ops, you need to compile them first by make under each ops subfolder (check Makefile). Update arch in the Makefiles for different CUDA Compute Capability that suits your GPU if necessary.
Data Preprocessing
The data preprocessing scripts are included in utils/data_preparation. Please follow the instructions in the README.md of each subdirectory.
Training and Evaluation
First download the ImageNet pretrained ResNet model from here and put it in pretrained_models/ImageNet-ResNet34.npz.
To train the model for Jester dataset, rename command_train.sh.jester.experiment to be command_train.sh and simply execute the shell script command_train.sh. Batch size, learning rate etc are adjustable.
sh command_train.sh
To evaluate the model, rename command_evaluate.sh.jester.experiment to be command_evaluate.sh and simply execute the shell script command_evaluate.sh.
sh command_evaluate.sh
To test the model, rename command_test.sh.jester.experiment to be command_test.sh and simply execute the shell script command_test.sh.
sh command_test.sh
A pre-trained model with ResNet-34 as backbone on Jester dataset is provided here for download.
For Something-Something dataset, the train, evaluation and test command files are command_train.sh.something.something.experiment, command_evaluate.sh.something.something.experiment and command_test.sh.something.something.experiment.
A pre-trained model with ResNet-34 as backbone on Something-Something dataset is provided here for download.
License
Our code is released under CC BY-NC-SA-4.0 License (see LICENSE file for details).
Related Projects
- MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences by Liu et al. (ICCV 2019 Oral Presentation). Code and data released in GitHub.
- FlowNet3D: Learning Scene Flow in 3D Point Clouds by Liu et al. (CVPR 2019). Code and data released in GitHub.
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation by Qi et al. (CVPR 2017 Oral Presentation). Code and data released in GitHub.