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wuzhe71 / CPD

Code of Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (CVPR2019)
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Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (CVPR2019)

Our model ranks first in the challenging SOC benchmark up to now (2019.11.6).

Requirements:

python2.7, pytorch 0.4.0

Usage

Modify the pathes of backbone and datasets, then run test_CPD.py

Pre-trained model

VGG16 backbone: google drive, BaiduYun (code: gb5u)

ResNet50 backbone: google drive, BaiduYun (code: klfd)

Pre-computed saliency maps

VGG16 backbone: google drive

ResNet50 backbone: google drive

Performance

Maximum F-measure

Model FPS ECSSD HKU-IS DUT-OMRON DUTS-TEST PASCAL-S
PiCANet 7 0.931 0.921 0.794 0.851 0.862
CPD 66 0.936 0.924 0.794 0.864 0.866
PiCANet-R 5 0.935 0.919 0.803 0.860 0.863
CPD-R 62 0.939 0.925 0.797 0.865 0.864

MAE

Model ECSSD HKU-IS DUT-OMRON DUTS-TEST PASCAL-S
PiCANet 0.046 0.042 0.068 0.054 0.076
CPD 0.040 0.033 0.057 0.043 0.074
PiCANet-R 0.046 0.043 0.065 0.051 0.075
CPD-R 0.037 0.034 0.056 0.043 0.072

Shadow Detection

pre-computed maps: google drive

Performance

BER

Model SBU ISTD UCF
DSC 5.59 8.24 8.10
CPD 4.19 6.76 7.21

Citation

@InProceedings{Wu_2019_CVPR,
author = {Wu, Zhe and Su, Li and Huang, Qingming},
title = {Cascaded Partial Decoder for Fast and Accurate Salient Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}