tcyhx/APN

Code for "Active Perception Network for Salient Object Detection"

Active Perception Network for Salient Object Detection

Introduction

To get better saliency maps for salient object detection, recent methods fuse features from different levels of convolutional neural networks and have achieved remarkable progress. However, the differences between different feature levels bring difficulties to the fusion process, thus it may lead to unsatisfactory saliency predictions. To address this issue, we propose Active Perception Network (APN) to enhance inter-feature consistency for salient object detection. First, Mutual Projection Module (MPM) is developed to fuse different features, which uses high-level features as guided information to extract complementary components from low-level features, and can suppress background noises and improve semantic consistency. Self Projection Module (SPM) is designed to further refine the fused features, which can be considered as the extended version of residual connection. Features that pass through SPM can produce more accurate saliency maps. Finally, we propose Head Projection Module (HPM) to aggregate global information, which brings strong semantic consistency to the whole network. Comprehensive experiments on six benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches on different evaluation metrics.

Prerequisites

• Python 3.5
• Pytorch 1.0
• OpenCV
• Numpy
• TensorboardX
• Apex

Clone repository

git clone git@github.com:weijun88/APN.git
cd APN/

Download dataset

Download the following datasets and unzip them into data folder

• SOD
• PASCAL-S
• ECSSD
• HKU-IS
• DUT-OMRON
• DUTS

Download model

If you want to test the performance of APN, please download the following model into out folder. If you want to train your own model, please download resnet50 into out folder as pretrained model

Training

    cd src/
    python3 train.py

• ResNet-50 is used as the backbone of APN and DUTS-TR is used to train the model
• batch=64, lr=0.1, momen=0.9, decay=5e-4, epoch=32
• Warm-up and linear decay strategies are used to change the learning rate (lr)
• After training, the result models will be saved in out folder and log files will be saved in log folder

Testing

    python3 test.py

• After testing, saliency maps of SOD, PASCAL-S, ECSSD, HKU-IS, DUT-OMRON, DUTS-TE will be saved in eval/APN/ folder in *.png folder.

Evaluation

• To evaluate the performace of APN, please use MATLAB to run main.m

• Quantitative comparisons

• Qualitative comparisons