A Simple Pooling-Based Design for Real-Time Salient Object Detection

CVPR 2019 Jiang-Jiang LiuQibin HouMing-Ming ChengJiashi FengJianmin Jiang

We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects... (read more)

PDF Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Salient Object Detection DUT-OMRON PoolNet (VGG-16) MAE 0.053 # 1
F-measure 0.833 # 1
Salient Object Detection DUTS-TE PoolNet (VGG-16) MAE 0.036 # 1
F-measure 0.892 # 1
Salient Object Detection ECSSD PoolNet (VGG-16) MAE 0.038 # 2
F-measure 0.945 # 1
Salient Object Detection HKU-IS PoolNet (VGG-16) MAE 0.03 # 1
F-measure 0.935 # 1
Salient Object Detection PASCAL-S PoolNet (VGG-16) MAE 0.065 # 1
F-measure 0.88 # 1
Salient Object Detection SOD PoolNet (VGG-16) MAE 0.102 # 1
F-measure 0.882 # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet