Delving into Salient Object Subitizing and Detection

Research output: Research - peer-review32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1059-1067
ISBN (Print)9781538610329
StatePublished - Oct 2017

Publication series

Name
ISSN (Print)1550-5499

Conference

Title16th IEEE International Conference on Computer Vision (ICCV 2017)
PlaceItaly
CityVenice
Period22 - 29 October 2017

Abstract

Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps. Moreover, the proposed method is an unconstrained method able to handle images with/without salient objects. Finally, we show state-of-theart performance on different salient object datasets.

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Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Delving into Salient Object Subitizing and Detection. / He, Shengfeng; Jiao, Jianbo; Zhang, Xiaodan; Han, Guoqiang; Lau, Rynson W.H.

Proceedings - 2017 IEEE International Conference on Computer Vision. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1059-1067 8237382.

Research output: Research - peer-review32_Refereed conference paper (with ISBN/ISSN)