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Volumn 39, Issue 1, 2017, Pages 189-203

Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

Author keywords

object detection; Weakly supervised learning

Indexed keywords

COMPUTER VISION; ITERATIVE METHODS; LEARNING SYSTEMS; LOCATION; LOCKS (FASTENERS); NEURAL NETWORKS; OBJECT DETECTION; SUPERVISED LEARNING;

EID: 85003782026     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2016.2535231     Document Type: Article
Times cited : (419)

References (56)
  • 3
    • 77949961607 scopus 로고    scopus 로고
    • Why modern CPUs are starving, what can be doneabout it
    • F. Alted, "Why modern CPUs are starving, what can be doneabout it, " Comput. Sci. Eng., vol. 12, no. 2, pp. 68-71, 2010.
    • (2010) Comput. Sci. Eng. , vol.12 , Issue.2 , pp. 68-71
    • Alted, F.1
  • 7
    • 84894906270 scopus 로고    scopus 로고
    • Object, action classificationwith latent window parameters
    • H. Bilen, V. Namboodiri, L. Van Gool, "Object, action classificationwith latent window parameters, " Int. J. Comput. Vis., vol. 106, no. 3, pp. 237-251, 2014.
    • (2014) Int. J. Comput. Vis. , vol.106 , Issue.3 , pp. 237-251
    • Bilen, H.1    Namboodiri, V.2    Van Gool, L.3
  • 10
    • 0141607824 scopus 로고    scopus 로고
    • Latent Dirichlet allocation
    • D. Blei, A. Ng, M. Jordan, "Latent Dirichlet allocation, " J. Mach. Learn., vol. 3, pp. 993-1022, 2003.
    • (2003) J. Mach. Learn. , vol.3 , pp. 993-1022
    • Blei, D.1    Ng, A.2    Jordan, M.3
  • 11
    • 84959186112 scopus 로고    scopus 로고
    • Unsupervised objectdiscovery, localization in the wild: Part-based matching withbottom-up region proposals
    • M. Cho, S. Kwak, C. Schmid, J. Ponce, "Unsupervised objectdiscovery, localization in the wild: Part-based matching withbottom-up region proposals, " in Proc. IEEE Conf. Comput. Vis. PatternRecog., 2015, pp. 1201-1210.
    • (2015) Proc IEEE Conf. Comput. Vis. PatternRecog. , pp. 1201-1210
    • Cho, M.1    Kwak, S.2    Schmid, C.3    Ponce, J.4
  • 13
  • 15
    • 33745831956 scopus 로고    scopus 로고
    • Weakly supervised learning ofpart-based spatial models for visual object recognition
    • D. Crandall, D. Huttenlocher, "Weakly supervised learning ofpart-based spatial models for visual object recognition, " in Proc. 9th Eur. Conf. Comput. Vis., 2006, pp. 16-29.
    • (2006) Proc. 9th Eur. Conf. Comput. Vis. , pp. 16-29
    • Crandall, D.1    Huttenlocher, D.2
  • 17
    • 84867062047 scopus 로고    scopus 로고
    • Weakly supervised localizationand learning with generic knowledge
    • T. Deselaers, B. Alexe, V. Ferrari, "Weakly supervised localizationand learning with generic knowledge, " Int. J. Comput. Vis., vol. 100, no. 3, pp. 257-293, 2012.
    • (2012) Int. J. Comput. Vis. , vol.100 , Issue.3 , pp. 257-293
    • Deselaers, T.1    Alexe, B.2    Ferrari, V.3
  • 18
    • 0030649484 scopus 로고    scopus 로고
    • Solving the multipleinstance problem with axis-parallel rectangles
    • T. Dietterich, R. Lathrop, T. Lozano-Perez, "Solving the multipleinstance problem with axis-parallel rectangles, " Artif. Intell., vol. 89, no. 1-2, pp. 31-71, 1997.
    • (1997) Artif. Intell. , vol.89 , Issue.1-2 , pp. 31-71
    • Dietterich, T.1    Lathrop, R.2    Lozano-Perez, T.3
  • 19
    • 62949172236 scopus 로고    scopus 로고
    • Taking the bite out ofautomatic naming of characters in TV video
    • M. Everingham, J. Sivic, A. Zisserman, "Taking the bite out ofautomatic naming of characters in TV video, " Image Vis. Comput., vol. 27, no. 5, pp. 545-559, 2009.
    • (2009) Image Vis. Comput. , vol.27 , Issue.5 , pp. 545-559
    • Everingham, M.1    Sivic, J.2    Zisserman, A.3
  • 25
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latentsemantic analysis
    • T. Hofmann, "Unsupervised learning by probabilistic latentsemantic analysis, " Mach. Learn., vol. 42, no. 1/2, pp. 177-196, 2001.
    • (2001) Mach. Learn. , vol.42 , Issue.1-2 , pp. 177-196
    • Hofmann, T.1
  • 26
    • 78649317568 scopus 로고    scopus 로고
    • Product quantization fornearest neighbor search
    • Jan.
    • H. Jegou, M. Douze, C. Schmid, "Product quantization fornearest neighbor search, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 1, pp. 117-128, Jan. 2011.
    • (2011) IEEE Trans. Pattern Anal. Mach. Intell. , vol.33 , Issue.1 , pp. 117-128
    • Jegou, H.1    Douze, M.2    Schmid, C.3
  • 28
    • 84856671399 scopus 로고    scopus 로고
    • Unsupervised detection of regions ofinterest using iterative link analysis
    • G. Kim, A. Torralba, "Unsupervised detection of regions ofinterest using iterative link analysis, " in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 4-2.
    • (2009) Proc. Adv. Neural Inf. Process. Syst. , pp. 4
    • Kim, G.1    Torralba, A.2
  • 31
    • 70350621774 scopus 로고    scopus 로고
    • Efficient subwindowsearch: A branch, bound framework for object localization
    • Dec.
    • C. Lampert, M. Blaschko, T. Hofmann, "Efficient subwindowsearch: A branch, bound framework for object localization, "IEEE Trans. Pattern Analy. Mach. Intell., vol. 31, no. 12, pp. 2129-2142, Dec. 2009.
    • (2009) IEEE Trans. Pattern Analy. Mach. Intell. , vol.31 , Issue.12 , pp. 2129-2142
    • Lampert, C.1    Blaschko, M.2    Hofmann, T.3
  • 32
    • 33845572523 scopus 로고    scopus 로고
    • Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories
    • S. Lazebnik, C. Schmid, J. Ponce, "Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories, "in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2006, pp. 2169-2178.
    • (2006) Proc IEEE Conf. Comput. Vis. Pattern Recog. , pp. 2169-2178
    • Lazebnik, S.1    Schmid, C.2    Ponce, J.3
  • 33
    • 17444406259 scopus 로고    scopus 로고
    • Smooth minimization of non-smooth functions
    • Y. Nesterov, "Smooth minimization of non-smooth functions, "Math. Program., vol. 103, no. 1, pp. 127-152, 2005.
    • (2005) Math. Program. , vol.103 , Issue.1 , pp. 127-152
    • Nesterov, Y.1
  • 34
    • 77953182042 scopus 로고    scopus 로고
    • Weaklysupervised discriminative localization, classification: A jointlearning process
    • M. Nguyen, L. Torresani, F. de la Torre, C. Rother, "Weaklysupervised discriminative localization, classification: A jointlearning process, " in Proc. Int. Conf. Comput. Vis., 2009, pp. 1925-1932.
    • (2009) Proc. Int. Conf. Comput. Vis. , pp. 1925-1932
    • Nguyen, M.1    Torresani, L.2    De La Torre, F.3    Rother, C.4
  • 35
    • 84856650974 scopus 로고    scopus 로고
    • Scene recognition, weakly supervisedobject localization with deformable part-based models
    • M. Pandey, S. Lazebnik, "Scene recognition, weakly supervisedobject localization with deformable part-based models, " inProc. Int. Conf. Comput. Vis., 2011, pp. 1307-1314.
    • (2011) Proc. Int. Conf. Comput. Vis. , pp. 1307-1314
    • Pandey, M.1    Lazebnik, S.2
  • 39
    • 84883487458 scopus 로고    scopus 로고
    • Image classificationwith the Fisher vector: Theory, practice
    • J. Sanchez, F. Perronnin, T. Mensink, J. Verbeek, "Image classificationwith the Fisher vector: Theory, practice, " Int. J. Comput. Vis., vol. 105, no. 3, pp. 222-245, 2013.
    • (2013) Int. J. Comput. Vis. , vol.105 , Issue.3 , pp. 222-245
    • Sanchez, J.1    Perronnin, F.2    Mensink, T.3    Verbeek, J.4
  • 40
    • 84898784069 scopus 로고    scopus 로고
    • Bayesian joint topic modellingfor weakly supervised object localisation
    • Z. Shi, T. Hospedales, T. Xiang, "Bayesian joint topic modellingfor weakly supervised object localisation, " in Proc. Int. Conf. Comput. Vis., 2013, pp. 2984-2991.
    • (2013) Proc. Int. Conf. Comput. Vis. , pp. 2984-2991
    • Shi, Z.1    Hospedales, T.2    Xiang, T.3
  • 43
    • 84867866572 scopus 로고    scopus 로고
    • In defence of negative miningfor annotating weakly labelled data
    • P. Siva, C. Russell, T. Xiang, "In defence of negative miningfor annotating weakly labelled data, " in Proc. Eur. Conf. Comput. Vis., 2012, pp. 594-608.
    • (2012) Proc. Eur. Conf. Comput. Vis. , pp. 594-608
    • Siva, P.1    Russell, C.2    Xiang, T.3
  • 45
    • 84856651319 scopus 로고    scopus 로고
    • Weakly supervised object detector learningwith model drift detection
    • P. Siva, T. Xiang, "Weakly supervised object detector learningwith model drift detection, " in Proc. Int. Conf. Comput. Vis., 2011, pp. 343-350.
    • (2011) Proc. Int. Conf. Comput. Vis. , pp. 343-350
    • Siva, P.1    Xiang, T.2
  • 52
    • 80052905596 scopus 로고    scopus 로고
    • Large-scale live activelearning: Training object detectors with crawled data andcrowds
    • S. Vijayanarasimhan, K. Grauman, "Large-scale live activelearning: Training object detectors with crawled data andcrowds, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 1449-1456.
    • (2011) Proc IEEE Conf. Comput. Vis. Pattern Recog. , pp. 1449-1456
    • Vijayanarasimhan, S.1    Grauman, K.2
  • 53
    • 84906334472 scopus 로고    scopus 로고
    • Weakly supervisedobject localization with latent category learning
    • C. Wang, W. Ren, K. Huang, T. Tan, "Weakly supervisedobject localization with latent category learning, " in Proc. 13thEur. Conf. Comput. Vis., 2014, pp. 431-445.
    • (2014) Proc. 13thEur. Conf. Comput. Vis. , pp. 431-445
    • Wang, C.1    Ren, W.2    Huang, K.3    Tan, T.4
  • 56
    • 84906489617 scopus 로고    scopus 로고
    • Edge boxes: Locating object proposalsfrom edges
    • C. Zitnick, P. Dollar, "Edge boxes: Locating object proposalsfrom edges, " in Proc. Eur. Conf. Comput. Vis., 2014, pp. 391-405.
    • (2014) Proc. Eur. Conf. Comput. Vis. , pp. 391-405
    • Zitnick, C.1    Dollar, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.