Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

Han, J; Zhang, D; Cheng, G; Liu, N; Xu, D

Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

Han, J Zhang, D Cheng, G Liu, N Xu, D

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Magazine, 2018, 35 (1), pp. 84 - 100
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Han, J	en_US
dc.contributor.author	Zhang, D	en_US
dc.contributor.author	Cheng, G	en_US
dc.contributor.author	Liu, N	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IEEE Signal Processing Magazine, 2018, 35 (1), pp. 84 - 100	en_US
dc.identifier.issn	1053-5888	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129683
dc.description.abstract	© 2017 IEEE. Object detection, including objectness detection (OD), salient object detection (SOD), and category-specific object detection (COD), is one of the most fundamental yet challenging problems in the computer vision community. Over the last several decades, great efforts have been made by researchers to tackle this problem, due to its broad range of applications for other computer vision tasks such as activity or event recognition, content-based image retrieval and scene understanding, etc. While numerous methods have been presented in recent years, a comprehensive review for the proposed high-quality object detection techniques, especially for those based on advanced deep-learning techniques, is still lacking. To this end, this article delves into the recent progress in this research field, including 1) definitions, motivations, and tasks of each subdirection; 2) modern techniques and essential research trends; 3) benchmark data sets and evaluation metrics; and 4) comparisons and analysis of the experimental results. More importantly, we will reveal the underlying relationship among OD, SOD, and COD and discuss in detail some open questions as well as point out several unsolved challenges and promising future works.	en_US
dc.relation.ispartof	IEEE Signal Processing Magazine	en_US
dc.relation.isbasedon	10.1109/MSP.2017.2749125	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	35	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	35	en_US

Abstract:

© 2017 IEEE. Object detection, including objectness detection (OD), salient object detection (SOD), and category-specific object detection (COD), is one of the most fundamental yet challenging problems in the computer vision community. Over the last several decades, great efforts have been made by researchers to tackle this problem, due to its broad range of applications for other computer vision tasks such as activity or event recognition, content-based image retrieval and scene understanding, etc. While numerous methods have been presented in recent years, a comprehensive review for the proposed high-quality object detection techniques, especially for those based on advanced deep-learning techniques, is still lacking. To this end, this article delves into the recent progress in this research field, including 1) definitions, motivations, and tasks of each subdirection; 2) modern techniques and essential research trends; 3) benchmark data sets and evaluation metrics; and 4) comparisons and analysis of the experimental results. More importantly, we will reveal the underlying relationship among OD, SOD, and COD and discuss in detail some open questions as well as point out several unsolved challenges and promising future works.

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http://hdl.handle.net/10453/129683