Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107957
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Type: | Conference paper |
Title: | Image co-localization by mimicking a good detector’s confidence score distribution |
Author: | Li, Y. Liu, L. Shen, C. van den Hengel, A. |
Citation: | Lecture Notes in Artificial Intelligence, 2016 / Leibe, B., Matas, J., Sebe, N., Welling, M. (ed./s), vol.9906 LNCS, pp.19-34 |
Publisher: | Springer International Publishing |
Issue Date: | 2016 |
Series/Report no.: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9906 |
ISBN: | 9783319464749 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 14th European Conference on Computer Vision (ECCV 2016) (11 Oct 2016 - 14 Oct 2016 : Amsterdam, Netherlands) |
Editor: | Leibe, B. Matas, J. Sebe, N. Welling, M. |
Statement of Responsibility: | Yao Li, Lingqiao Liu, Chunhua Shen, and Anton van den Hengel |
Abstract: | Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-arts. |
Keywords: | Image co-localization, unsupervised object discovery |
Rights: | © Springer International Publishing AG 2016 |
DOI: | 10.1007/978-3-319-46475-6_2 |
Grant ID: | http://purl.org/au-research/grants/arc/FT120100969 |
Published version: | http://dx.doi.org/10.1007/978-3-319-46475-6_2 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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RA_hdl_107957.pdf Restricted Access | Restricted Access | 9.88 MB | Adobe PDF | View/Open |
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