메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1863-1870

Multiclass transfer learning from unconstrained priors

Author keywords

[No Author keywords available]

Indexed keywords

JOINT OPTIMIZATION; LEARNING METHODS; LEARNING PROBLEM; MULTI-CLASS; MULTI-KERNEL; OBJECT CATEGORIZATION; OBJECT CATEGORY DETECTION; TRANSFER LEARNING; VISUAL RECOGNITION;

EID: 84856679717     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126454     Document Type: Conference Paper
Times cited : (76)

References (37)
  • 1
    • 34547995321 scopus 로고    scopus 로고
    • Multiple kernel learning, conic duality, and the SMO algorithm
    • F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML, 2004. 3
    • (2004) ICML , pp. 3
    • Bach, F.R.1    Lanckriet, G.R.G.2    Jordan, M.I.3
  • 2
    • 43049174575 scopus 로고    scopus 로고
    • Surf: Speeded up robust features
    • 6
    • H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. CVIU, 110:346-359, 2008. 6
    • (2008) CVIU , vol.110 , pp. 346-359
    • Bay, H.1    Ess, A.2    Tuytelaars, T.3    Gool, L.V.4
  • 3
    • 36849053972 scopus 로고    scopus 로고
    • Discriminative learning for differing training and test distributions
    • S. Bickel, M. Brckner, and T. Scheffer. Discriminative learning for differing training and test distributions. In ICML, 2007. 1
    • (2007) ICML , pp. 1
    • Bickel, S.1    Brckner, M.2    Scheffer, T.3
  • 4
    • 36849014901 scopus 로고    scopus 로고
    • Representing shape with a spatial pyramid kernel
    • A. Bosch, A. Zisserman, and Munoz. Representing shape with a spatial pyramid kernel. In CIVR, pages 40 1-408, 2007. 6
    • (2007) CIVR , vol.6 , pp. 401-408
    • Bosch, A.1    Zisserman, A.2    Munoz3
  • 6
    • 0010442827 scopus 로고    scopus 로고
    • On the algorithmic implementation of multiclass kernel-based vector machines
    • 3
    • K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. JMLR. 2:265-292, 2002. 3
    • (2002) JMLR , vol.2 , pp. 265-292
    • Crammer, K.1    Singer, Y.2
  • 7
    • 71149083696 scopus 로고    scopus 로고
    • Eigentransfer: A unified framework for transfer learning
    • W. Dai, O. Jin, G.-R. Xue, Q. Yang, and Y. Yu. Eigentransfer: A unified framework for transfer learning. In ICML, 2009. 1
    • (2009) ICML , pp. 1
    • Dai, W.1    Jin, O.2    Xue, G.-R.3    Yang, Q.4    Yu, Y.5
  • 8
    • 84860513476 scopus 로고    scopus 로고
    • Frustratingly easy domain adaptation
    • H. Daume III. Frustratingly easy domain adaptation. InACL, 2007. 1
    • (2007) InACL , vol.1
    • Daume III, H.1
  • 9
    • 78149302207 scopus 로고    scopus 로고
    • What does classifying more than 10,000 image categories tell us?
    • 1
    • J. Deng, A. C. Berg, K. Li, and L. Fei-Fei. What does classifying more than 10,000 image categories tell us? In ECCV, pages 71-84, 2010. 1
    • (2010) ECCV , pp. 71-84
    • Deng, J.1    Berg, A.C.2    Li, K.3    Fei-Fei, L.4
  • 10
    • 70450185098 scopus 로고    scopus 로고
    • Domain transfer svm for video concept detection
    • L. Duan, I. Tsang, D. Xu, and S. Maybank. Domain transfer svm for video concept detection. In CVPR, 2009. 1
    • (2009) CVPR , vol.1
    • Duan, L.1    Tsang, I.2    Xu, D.3    Maybank, S.4
  • 11
    • 56049109363 scopus 로고    scopus 로고
    • Modeling transfer relationships between learning tasks for improved inductive transfer
    • E. Eaton, M. desJardins, and T. Lane. Modeling transfer relationships between learning tasks for improved inductive transfer. In ECML, 2008. 1
    • (2008) ECML , vol.1
    • Eaton, E.1    DesJardins, M.2    Lane, T.3
  • 12
    • 70450207704 scopus 로고    scopus 로고
    • Describing objects by their attributes
    • A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009. 1, 4
    • (2009) CVPR , vol.1 , pp. 4
    • Farhadi, A.1    Endres, I.2    Hoiem, D.3    Forsyth, D.4
  • 13
    • 33144466753 scopus 로고    scopus 로고
    • One-shot learning of object categories
    • 1
    • L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. PAM1, 28:594-611, 2006. 1
    • (2006) PAMI , vol.28 , pp. 594-611
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 14
    • 85067032737 scopus 로고    scopus 로고
    • On feature combination for multiclass object classification
    • P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In ICCV, 2009. 5, 6
    • (2009) ICCV , vol.5 , pp. 6
    • Gehler, P.1    Nowozin, S.2
  • 15
    • 34948904828 scopus 로고    scopus 로고
    • Caltech 256 object category dataset
    • California Institue of Technology
    • G. Griffin, A. Holub, and P. Perona. Caltech 256 object category dataset. Technical Report UCB/CSD-04-1366, California Institue of Technology, 2007. 1, 5, 6
    • (2007) Technical Report UCB/CSD-04-1366 , vol.1 , Issue.5 , pp. 6
    • Griffin, G.1    Holub, A.2    Perona, P.3
  • 17
    • 70450172710 scopus 로고    scopus 로고
    • Learning to detect unseen object classes by between-class attribute transfer
    • C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009. 1, 4, 6
    • (2009) CVPR , vol.1 , Issue.4 , pp. 6
    • Lampert, C.H.1    Nickisch, H.2    Harmeling, S.3
  • 18
    • 85162513516 scopus 로고    scopus 로고
    • Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
    • L.-J. Li, H. Su, E. P. Xing, and L. Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. In NIPS, 2010. 4
    • (2010) NIPS , vol.4
    • Li, L.-J.1    Su, H.2    Xing, E.P.3    Fei-Fei, L.4
  • 19
    • 0033284915 scopus 로고    scopus 로고
    • Object recognition from local scale-invariant features
    • D. G. Lowe. Object recognition from local scale-invariant features. In ICCV, 1999. 6
    • (1999) ICCV , vol.6
    • Lowe, D.G.1
  • 20
    • 0036647193 scopus 로고    scopus 로고
    • Multiresolution grayscale and rotation invariant texture classification with local binary patterns
    • 6
    • T. Ojala, M. Pietikinen, and T. Menp. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. PAM1, 24:971-987, 2002. 6
    • (2002) PAMI , vol.24 , pp. 971-987
    • Ojala, T.1    Pietikinen, M.2    Menp, T.3
  • 21
    • 77955993905 scopus 로고    scopus 로고
    • Online-batch strongly convex multi kernel learning
    • F. Orabona, L. .lie, and B. Caputo. Online-batch strongly convex multi kernel learning. In CVPR, 2010. 1, 3, 4
    • (2010) CVPR , vol.1 , Issue.3 , pp. 4
    • Orabona, F.1    Jie, L.2    Caputo, B.3
  • 22
    • 38949193299 scopus 로고    scopus 로고
    • Why is real-world visual object recognition hard?
    • N. Pinto, D. D. Cox, and J. J. DiCarlo. Why is Real-World Visual Object Recognition Hard? PLoS Comput BioI, 4( 1), 2008. 6
    • (2008) PLoS Comput BioI , vol.4 , Issue.1 , pp. 6
    • Pinto, N.1    Cox, D.D.2    DiCarlo, J.J.3
  • 23
    • 51949094374 scopus 로고    scopus 로고
    • Transfer learning for image classification with sparse prototype representations
    • A. Quattoni, M. Collins, and T. Darrell. Transfer learning for image classification with sparse prototype representations. In CVPR, 2008. 1
    • (2008) CVPR , vol.1
    • Quattoni, A.1    Collins, M.2    Darrell, T.3
  • 24
    • 51949106645 scopus 로고    scopus 로고
    • Self-taught learning: Transfer learning from unlabeled data
    • 1
    • R. Raina, A. Battle, H. Lee, and B. P. A. Y. Ng. Self-taught learning: Transfer learning from unlabeled data. In ICML, 2007. 1
    • (2007) ICML
    • Raina, R.1    Battle, A.2    Lee, H.3    Ng, B.P.A.Y.4
  • 25
    • 78349281072 scopus 로고    scopus 로고
    • What helps where? and why? semantic relatedness for knowledge transfer
    • I
    • M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele. What helps where? and why? semantic relatedness for knowledge transfer. In CVPR, 20 10. I
    • CVPR , vol.20 , Issue.10
    • Rohrbach, M.1    Stark, M.2    Szarvas, G.3    Gurevych, I.4    Schiele, B.5
  • 27
    • 78149301639 scopus 로고    scopus 로고
    • Adapting visual category models to new domains
    • K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV. 2010. 1
    • (2010) ECCV. , vol.1
    • Saenko, K.1    Kulis, B.2    Fritz, M.3    Darrell, T.4
  • 28
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • 6
    • R. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine L earning, 37:297-336, 1999. 6
    • (1999) Machine L earning , vol.37 , pp. 297-336
    • Schapire, R.1    Singer, Y.2
  • 29
    • 77953222843 scopus 로고    scopus 로고
    • A shape-based object class model for knowledge transfer
    • M. Stark, M. Goesele, and B. Schiele. A shape-based object class model for knowledge transfer. In ICCV, 2009. 1
    • (2009) ICCV , vol.1
    • Stark, M.1    Goesele, M.2    Schiele, B.3
  • 30
    • 77956008920 scopus 로고    scopus 로고
    • Optimizing one-shot recognition with micro-set learning
    • 1
    • K. Tang, M. Tappen, R. Sukthankar, and C. Lampert. Optimizing one-shot recognition with micro-set learning. In CVPR, 2010. 1
    • (2010) CVPR
    • Tang, K.1    Tappen, M.2    Sukthankar, R.3    Lampert, C.4
  • 31
    • 77956005674 scopus 로고    scopus 로고
    • Safety in numbers: Learning categories from few examples with multi model knowledge transfer
    • T. Tommasi, F. Orabona, and B. Caputo. Safety in numbers: Learning categories from few examples with multi model knowledge transfer. In CVPR, 20 10. 1, 4, 5
    • (2010) CVPR , vol.1 , Issue.4 , pp. 5
    • Tommasi, T.1    Orabona, F.2    Caputo, B.3
  • 32
    • 78149355981 scopus 로고    scopus 로고
    • Efficient object category recognition using classemes
    • 5
    • L. Torresani, M. Szummer, and A. Fitzgibbon. Efficient object category recognition using classemes. In ECCV, pages 776-789, 20 10. 4, 5
    • (2010) ECCV , vol.4 , pp. 776-789
    • Torresani, L.1    Szummer, M.2    Fitzgibbon, A.3
  • 33
    • 14344250451 scopus 로고    scopus 로고
    • Support vector machine learning for interdependent and structured output spaces
    • 3
    • I. Tsochantaridis, T. Hofmarrn, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004. 2, 3
    • (2004) ICML , vol.2
    • Tsochantaridis, I.1    Hofmarrn, T.2    Joachims, T.3    Altun, Y.4
  • 34
    • 34948876368 scopus 로고    scopus 로고
    • Human detection via classification on riemannian manifolds
    • O. Tuzel, F. Porikli, and P. Meer. Human detection via classification on riemannian manifolds. In CVPR, 2007. 6
    • (2007) CVPR , vol.6
    • Tuzel, O.1    Porikli, F.2    Meer, P.3
  • 35
    • 84856648103 scopus 로고    scopus 로고
    • Semantic modeling of natural scenes for content-based image retrieval
    • J. Vogel and B. Schiele. Semantic modeling of natural scenes for content-based image retrieval. LJCV, 2008. 4
    • (2008) LJCV , vol.4
    • Vogel, J.1    Schiele, B.2
  • 36
    • 77955998024 scopus 로고    scopus 로고
    • Boosting for transfer learning with multiple sources
    • Y. Yao and G. Doretto. Boosting for transfer learning with multiple sources. In CVPR, 20 10. 1
    • (2010) CVPR , vol.1
    • Yao, Y.1    Doretto, G.2
  • 37
    • 33645035051 scopus 로고    scopus 로고
    • Model selection and estimation in regression with grouped variables
    • 3
    • M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. J. Roy. Stat. Society, 68:49-67, 2006. 3
    • (2006) J. Roy. Stat. Society , vol.68 , pp. 49-67
    • Yuan, M.1    Lin, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.