Delving into Salient Object Subitizing and Detection
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Computer Vision |
Subtitle of host publication | |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 1059-1067 |
ISBN (print) | 9781538610329, 9781538610336 |
Publication status | Published - Oct 2017 |
Publication series
Name | |
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ISSN (electronic) | 2380-7504 |
Conference
Title | 16th IEEE International Conference on Computer Vision, ICCV 2017 |
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Location | Venice Convention Center |
Place | Italy |
City | Venice |
Period | 22 - 29 October 2017 |
Link(s)
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.
Bibliographic Note
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 et al.
Proceedings - 2017 IEEE International Conference on Computer Vision: . Institute of Electrical and Electronics Engineers, Inc., 2017. p. 1059-1067 8237382.
Proceedings - 2017 IEEE International Conference on Computer Vision: . Institute of Electrical and Electronics Engineers, Inc., 2017. p. 1059-1067 8237382.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review