Keras Implementation (v1) for
Video salient object detection via fully convolutional networks, IEEE Trans. on Image Processing, 27(1):38-49, 2018
By Wenguan Wang and Jianbing Shen and Ling Shao
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Please install keras first
The model weights and results on DAVIS and FBMS datasets can be downloaded from
Baidu Wangpan:https://pan.baidu.com/s/1dE22MFR
password: jmph
or from google drive: https://drive.google.com/drive/u/0/folders/1mc6nnr8RrMZwAXjV0XS9Hv7QJE4quvkx
Please put the models under the folder 'videosalientobjectdetection' and run main.py.
Our results (MAE) are sightly improved over the original scores reported in our paper:
DAVIS: 0.0587 FBMS: 0.0619
You can find our results on UVSD dataset here: https://pan.baidu.com/s/1DUWHiIf-EGG1l-izaAf09Q
Our code for synthesizing video data from still image has been uploaded.
UnZip synthesizevideo.rar first.
Then install vlfeat toolbox first (in 'functions' folder).
Run synthesizevideo.m.
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If you find our method useful in your research, please consider citing the following papers:
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W. Wang, J. Shen, and L. Shao, Video salient object detection via fully convolutional networks, IEEE Trans. on Image Processing, 27(1):38-49, 2018
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W. Wang, J. Shen, and L. Shao, Consistent video saliency using local gradient flow optimization and global refinement, IEEE Trans. on Image Processing, 24(11):4185-4196, 2015
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W. Wang, J. Shen, R. Yang, and F. Porikli, Saliency-aware video object segmentation, IEEE Trans. on Pattern Analysis and Machine Intelligence, 40(1):20-33, 2018
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W. Wang, J. Shen, F. Porikli, Saliency-aware geodesic video object segmentation, IEEE CVPR, pp. 3395-3402, 2015
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See my new work in related areas:
(ECCV18)Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection,
(CVPR19) Learning Unsupervised Video Object Segmentation through Visual Attention,
(CVPR19 Oral, coming soon) Shifting More Attention to Video Salient Object Detection.
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Any comments, please email: wenguanwang.china@gmail.com