RGB-D saliency detection by multi-stream late fusion network

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationComputer Vision Systems
Subtitle of host publication11th International Conference, ICVS 2017, Shenzhen, China, July 10-13, 2017, Revised Selected Papers
PublisherSpringer, Cham
Pages459-468
ISBN (Electronic)978-3-319-68345-4
ISBN (Print)978-3-319-68344-7
Publication statusPublished - Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10528 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title11th International Conference on Computer Vision Systems, ICVS 2017
PlaceChina
CityShenzhen
Period10 - 13 July 2017

Abstract

In this paper we aim to address the problem of saliency detection on RGB-D image pairs based on a multi-stream late fusion network. With the prevalence of RGB-D sensors, leveraging additional depth information to facilitate saliency detection task has drawn increasing attention. However, the key challenge that how to fuse RGB data and depth data in an optimum manner is still under-studied. Conventional wisdom simply regards depth information as an undifferentiated channel and models RGB-D saliency detection by using existing RGB saliency detection models directly. However, this paradigm is incapable of capturing specific representations in depth modality and also powerless in fusing multi-modal information. In this paper, we address this problem by proposing a simple yet principled late fusion strategy carried out in conjunction with convolutional neural networks (CNNs). The proposed network is able to learn discriminant representations and explore the complementarity between RGB and depth modalities. Comprehensive experiments on two public datasets witness the benefits of the proposed RGB-D saliency detection network.

Research Area(s)

  • Convolutional neural networks, RGB-D, Saliency detection

Citation Format(s)

RGB-D saliency detection by multi-stream late fusion network. / Chen, Hao; Li, Youfu; Su, Dan.

Computer Vision Systems: 11th International Conference, ICVS 2017, Shenzhen, China, July 10-13, 2017, Revised Selected Papers. Springer, Cham, 2017. p. 459-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10528 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review