Code for paper in CVPR2019, 'Shifting More Attention to Video Salient Object Detection', Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, Jianbing Shen.

Contact: Deng-Ping Fan, Email: dengpingfan@mail.nankai.edu.cn

Abstract The last decade has witnessed a growing interest in video salient object detection (VSOD). However, the research community long-term lacked a well-established VSOD dataset representative of real dynamic scenes with high-quality annotations. To address this issue, we elaborately collected a visual-attention-consistent Densely Annotated VSOD (DAVSOD) dataset, which contains 226 videos with 23,938 frames that cover diverse realistic-scenes, objects, instances and motions. With corresponding real human eye-fixation data, we obtain precise ground-truths. This is the first work that explicitly emphasizes the challenge of saliency shift, i.e., the video salient object(s) may dynamically change. To further contribute the community a complete benchmark, we systematically assess 17 representative VSOD algorithms over seven existing VSOD datasets and our DAVSOD with totally ~84K frames (largest-scale). Utilizing three famous metrics, we then present a comprehensive and insightful performance analysis. Furthermore, we propose a baseline model. It is equipped with a saliencyshift-aware convLSTM, which can efficiently capture video saliency dynamics through learning human attention-shift behavior. Extensive experiments1 open up promising future directions for model development and comparison.

pre-computed saliency maps: http://dpfan.net/DAVSOD/.

1. Clone this repo into your computer

git clone https://github.com/DengPingFan/DAVSOD.git

2. Cd to DAVSOD/mycaffe-convlstm, follow the official instructions to build caffe. We provide our make file Makefile.config in folder DAVSOD/mycaffe-convlstm.

The code has been tested successfully on Ubuntu 16.04 with CUDA 8.0 and OpenCV 3.1.0

3. Make 'caffe'

make all -j8

4. Make 'pycaffe'

make pycaffe

5. Download pretrained caffemodel from my homepage or directly from [baidu pan](Fetch Code: pb0h)/ [google drive] and extract the .zip file under the root directory DAVSOD/model/. If you want to train the model start from scratch, you can download the basemodel from [baidu pan](Fetch Code:0xk4) or [google drive]

6. Put the test image in DAVSOD/Datasets/ and run generateTestList.py to get the test list. Then run SSAV_test.py to get the saliency maps. The results will be saved in DAVSOD/results/SSAV/.

7. You can also evaluate the model performance (S-measure[1], E-measure[2], F-measure and MAE) using our one-key matlab code main.m in DAVSOD/EvaluateTool/ directory.

[1]Structure-measure: A New Way to Evaluate the Foregournd Maps, ICCV2017, spotlight.

[2]Enhanced Alignment Measure for Binary Foreground Map Evaluation, IJCAI2018, Oral.

Note that: This version only provide the implicit manner for learning attention-shift. The explicit way to train this model will not be released due to the commercial purposes (Hua Wei, IIAI).

Quantitative comparisons

Quanlitative comparisons

If you find this useful, please cite the related works as follows:

@InProceedings{Fan_2019_CVPR,
   author = {Fan, Deng-Ping and Wang, Wenguan and Cheng, Ming-Ming and Shen, Jianbing}, 
   title = {Shifting More Attention to Video Salient Object Detection},
   booktitle = {IEEE CVPR},
   year = {2019}
}

@InProceedings{Wang_2018_CVPR,
	author = {Wang, Wenguan and Shen, Jianbing and Guo, Fang and Cheng, Ming-Ming and Borji, Ali},
	title = {Revisiting Video Saliency: A Large-Scale Benchmark and a New Model},
	booktitle = {IEEE CVPR},
	year = {2018}
}

@inproceedings{song2018pyramid,
  title={Pyramid dilated deeper ConvLSTM for video salient object detection},
  author={Song, Hongmei and Wang, Wenguan and Zhao, Sanyuan and Shen, Jianbing and Lam, Kin-Man},
  booktitle={ECCV},
  pages={715--731},
  year={2018}
}

##License

Copyright (c) 2019, Deng-Ping Fan
All rights reserved.

This code is for academic communication only and not for commercial purposes. 
If you want to use for commercial please contact me.

Redistribution and use in source with or without
modification, are permitted provided that the following conditions are
met:
		* Redistributions of source code must retain the above copyright
  		  notice, this list of conditions and the following disclaimer.
		* Redistributions in binary form must reproduce the above copyright
  		  notice, this list of conditions and the following disclaimer in
  		  the documentation and/or other materials provided with the distribution

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 	
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.

If you find any bugs, please contact Deng-Ping Fan (dengpingfan@mail.nankai.edu.cn).