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This repository includes the codes for our weakly-supervised video co-segmention. Please cite our paper if you use our code and model for your research.
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This code has been tested on Ubuntu 14.04 and MATLAB 2015a.
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Contact: Guangyu Zhong (guangyuzhonghikari at gmail dot com)
Semantic Co-segmentation in Videos. Yi-Hsuan Tsai*, Guangyu Zhong* and Ming-Hsuan Yang. European Conference on Computer Vision (ECCV), 2016. (* indicates equal contribution)
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Download and unzip the code.
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Install the attached caffe branch, as instructed at http://caffe.berkeleyvision.org/installation.html.
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Download the CNN model for feature extraction at http://vllab.ucmerced.edu/ytsai/CVPR16/pascal_segmentation.zip, then unzip the model folder under the caffe-cedn-dev/examples folder.
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Put your own videos in "Youtube_input" or another folder (you may need to change the corresponding paths).
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run demo_semantic_cosegment.m to generate tracklets.
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run demo_tracklets_feature.m to extract features for each tracklet.
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run demo_tracklets_submodular.m to select and merge tracklets.
- Currently this package only contains the implementation of our weakly-supervised video co-segmentation part.
- Please cite our paper if you find this work is useful.
@inproceedings{tsai2016semantic,
title={Semantic Co-segmentation in Videos},
author={Tsai, Yi-Hsuan and Zhong, Guangyu and Yang, Ming-Hsuan},
booktitle={European Conference on Computer Vision},
year={2016},
}
- Step 0: generate tracklets.
- Step 1 & 2: feature extraction and co-selection
- Check more results in our supplementary video.
