Delving into Salient Object Subitizing and Detection

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

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Original languageEnglish
Title of host publicationInternational Conference on Computer Vision (ICCV) 2017
StateAccepted/In press/Filed - 19 Jul 2017

Abstract

Subitizing (i.e., instant judgment 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.

Citation Format(s)

Delving into Salient Object Subitizing and Detection. / HE, Shengfeng; JIAO, Jianbo; ZHANG, Xiaodan; Han, Guoqiang; LAU, Rynson W H.

International Conference on Computer Vision (ICCV) 2017. 2017.

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