UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Add journal submission link:
https://arxiv.org/abs/2009.03075
Add performance of our UC-Net on DUT RGBD saliency testing dataset(https://github.com/jiwei0921/RGBD-SOD-datasets) (400 images):
https://drive.google.com/file/d/14LpM8yB-yKqQiV5sAtDhUqTKwMBFrTGv/view?usp=sharing
Our journal extension will coming soon. Please find links below for our results and ablation studies.
Code for our submission: https://drive.google.com/file/d/1Bz_vy2farSXEU2v1E23NT2s7Cm4dSqv3/view?usp=sharing, which includes:
1) Our CVAE model (hybrid loss), 2) Our ABP model, 3) the middle-fusion model, 4) the late-fusion model, 5) the GSCNN model, 6) the simple CVAE model.
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Install Pytorch
Download training data from: https://drive.google.com/file/d/1zslnkJaD_8h3UjxonBz0ESEZ2eguR_Zi/view?usp=sharing, and put it in folder "data"
Run ./train.py
Download the trained model from: https://drive.google.com/file/d/1nzGLnlmntTGbcaShfQvE6ouyfWJD-pIB/view?usp=sharing, and put it in folder "models"
Download the testing dataset from: https://drive.google.com/file/d/1n1bEfw3lzI6p8u1xaxEqnuEXgNqbAFTA/view?usp=sharing, and put it in folder "test_dataset"
Modify testing image path in "test.py" accordingly
Run ./test.py
Results of our model on six benchmark datasets can be found: https://drive.google.com/open?id=1NVJVU8dlf2d9h9T8ChXyNjZ5doWPYhjg or: 链接: https://pan.baidu.com/s/1M9_Bv16-tTnlgF6ayBmc6w 提取码: u8s5
Performance of our method can be found: https://drive.google.com/open?id=1vacU51eG7_r751lAsjKTPSGrdjzt_Z4H or: 链接: https://pan.baidu.com/s/1o6kFY8Y81_V-pftc8kTgUw 提取码: fqpd
Performance of competing methods can be found: https://drive.google.com/open?id=1NUMp_zKXSx8jc7u7HnPQmcYXtoiLWj6t or: 链接: https://pan.baidu.com/s/1g1dbwsGowLD_FFAx0ciSHw 提取码: sqar
Please cite our papers if you like our work:
@inproceedings{Zhang2020UCNet,
title={UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders},
author={Zhang, Jing and Fan, Deng-Ping and Dai, Yuchao and Anwar, Saeed and Sadat Saleh, Fatemeh and Zhang, Tong and Barnes, Nick},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
year={2020}
}
@article{zhang2021uncertainty,
title={Uncertainty Inspired RGB-D Saliency Detection},
author={Jing Zhang and Deng-Ping Fan and Yuchao Dai and Saeed Anwar and Fatemeh Saleh and Sadegh Aliakbarian and Nick Barnes},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021}
}
The complete RGB-D SOD benchmark can be found in this page:
http://dpfan.net/d3netbenchmark/
Please contact me for further problems or discussion: zjnwpu@gmail.com