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DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Traditional 1 salient object detection models often use hand-crafted features to formulate contrast and various prior knowledge, and then combine them artificially. In this work, we propose a novel end-to-end deep hierarchical saliency network (DHSNet) based on convolutional neural networks for detecting salient objects. DHSNet first makes a coarse global prediction by automatically learning various global structured saliency cues, including global contrast, objectness, compactness, and their
doi:10.1109/cvpr.2016.80
dblp:conf/cvpr/LiuH16
fatcat:7e5bu4fkp5ceflkpwffito5oky