Code for CVPR 2019 paper:
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, Fatih Porikli
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Our group co-attention achieves a further performance gain (81.1 mean J on DAVIS-16 dataset).
The pre-trained model, testing and training code:
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Install pytorch (version:1.0.1).
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Download the pretrained model. Run 'test_coattention_conf.py' and change the davis dataset path, pretrainde model path and result path.
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Run command: python test_coattention_conf.py --dataset davis --gpus 0
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Post CRF processing code: https://github.com/lucasb-eyer/pydensecrf
The pretrained weight can be download from GoogleDrive or BaiduPan, pass code: xwup.
The segmentation results on DAVIS, FBMS and Youtube-objects can be download from GoogleDrive or BaiduPan, pass code: q37f.
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Download all the training datasets, including MARA10K and DUT saliency datasets. Create a folder called images and put these two datasets into the folder.
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Download the deeplabv3 model from GoogleDrive. Put it into the folder pretrained/deep_labv3.
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Change the video path, image path and deeplabv3 path in train_iteration_conf.py. Create two txt files which store the saliency dataset name and DAVIS16 training sequences name. Change the txt path in PairwiseImg_video.py.
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Run command: python train_iteration_conf.py --dataset davis --gpus 0,1
If you find the code and dataset useful in your research, please consider citing:
@InProceedings{Lu_2019_CVPR,
author = {Lu, Xiankai and Wang, Wenguan and Ma, Chao and Shen, Jianbing and Shao, Ling and Porikli, Fatih},
title = {See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019} }
Saliency-Aware Geodesic Video Object Segmentation (CVPR15)
Learning Unsupervised Video Primary Object Segmentation through Visual Attention (CVPR19)
Any comments, please email: carrierlxk@gmail.com