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Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting
Ren, Weiqiang1,2; Huang, Kaiqi1,2; Tao, Dacheng3,4; Tan, Tieniu1,2
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2016-02-01
Volume38Issue:2Pages:405-416
SubtypeArticle
AbstractLocalizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations.
KeywordWeakly Supervised Localization Convolutional Networks Multiple Instance Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2015.2456908
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China(2012CB316302) ; National Natural Science Foundation of China(61322209 ; China Scholarship Council ; Australian Research Council(DP-140102164 ; 61175007) ; FT-130101457)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000369989600016
Citation statistics
Cited Times:21[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11342
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci CASIA, Ctr Res Intelligent Percept & Comp CRIPAC, 95 ZhongGuanCun East St, Beijing 100190, Peoples R China
2.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, 95 ZhongGuanCun East St, Beijing 100190, Peoples R China
3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, 81 Broadway St, Ultimo, NSW 2007, Australia
4.Univ Technol Sydney, Fac Engn & Informat Technol, 81 Broadway St, Ultimo, NSW 2007, Australia
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ren, Weiqiang,Huang, Kaiqi,Tao, Dacheng,et al. Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2016,38(2):405-416.
APA Ren, Weiqiang,Huang, Kaiqi,Tao, Dacheng,&Tan, Tieniu.(2016).Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,38(2),405-416.
MLA Ren, Weiqiang,et al."Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38.2(2016):405-416.
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