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Weakly-Supervised Salient Object Detection With Saliency Bounding Boxes.
- Source :
-
IEEE Transactions on Image Processing . 2021, Vol. 30, p4423-4435. 13p. - Publication Year :
- 2021
-
Abstract
- In this paper, we propose a novel form of weak supervision for salient object detection (SOD) based on saliency bounding boxes, which are minimum rectangular boxes enclosing the salient objects. Based on this idea, we propose a novel weakly-supervised SOD method, by predicting pixel-level pseudo ground truth saliency maps from just saliency bounding boxes. Our method first takes advantage of the unsupervised SOD methods to generate initial saliency maps and addresses the over/under prediction problems, to obtain the initial pseudo ground truth saliency maps. We then iteratively refine the initial pseudo ground truth by learning a multi-task map refinement network with saliency bounding boxes. Finally, the final pseudo saliency maps are used to supervise the training of a salient object detector. Experimental results show that our method outperforms state-of-the-art weakly-supervised methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PIXELS
*COMPUTER science
*DETECTORS
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 30
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077786
- Full Text :
- https://doi.org/10.1109/TIP.2021.3071691