Authors
Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, Jianmin Jiang
Publication date
2019
Conference
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
Pages
3917-3926
Description
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. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path-way. By adding FAMs after the fusion operations in the top-down pathway, coarse-level features from the GGM can be seamlessly merged with features at various scales. These two pooling-based modules allow the high-level semantic features to be progressively refined, yielding detail enriched saliency maps. Experiment results show that our proposed approach can more accurately locate the salient objects with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach is fast as well and can run at a speed of more than 30 FPS when processing a 300x400 image. Code can be found at http://mmcheng. net/poolnet/.
Total citations
20192020202120222023202415125212279289183
Scholar articles
JJ Liu, Q Hou, MM Cheng, J Feng, J Jiang - Proceedings of the IEEE/CVF conference on computer …, 2019