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Saliency Detection with Recurrent Fully Convolutional Networks

  • Linzhao Wang
  • Lijun Wang
  • Huchuan Lu
  • Pingping Zhang
  • Xiang Ruan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9908)

Abstract

Deep networks have been proved to encode high level semantic features and delivered superior performance in saliency detection. In this paper, we go one step further by developing a new saliency model using recurrent fully convolutional networks (RFCNs). Compared with existing deep network based methods, the proposed network is able to incorporate saliency prior knowledge for more accurate inference. In addition, the recurrent architecture enables our method to automatically learn to refine the saliency map by correcting its previous errors. To train such a network with numerous parameters, we propose a pre-training strategy using semantic segmentation data, which simultaneously leverages the strong supervision of segmentation tasks for better training and enables the network to capture generic representations of objects for saliency detection. Through extensive experimental evaluations, we demonstrate that the proposed method compares favorably against state-of-the-art approaches, and that the proposed recurrent deep model as well as the pre-training method can significantly improve performance.

Keywords

Saliency detection Recurrent fully convolutional network 

Notes

Acknowledgement

The work is supported by the National Natural Science Foundation of China under Grant 61528101 and Grant 61472060.

Supplementary material

419976_1_En_50_MOESM1_ESM.pdf (19.3 mb)
Supplementary material 1 (pdf 19807 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Linzhao Wang
    • 1
  • Lijun Wang
    • 1
  • Huchuan Lu
    • 1
  • Pingping Zhang
    • 1
  • Xiang Ruan
    • 2
  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina
  2. 2.TIWAKI CorporationIwakiJapan

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