/CPD

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

Primary LanguagePython

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}
}