메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1721-1728

Learning the easy things first: Self-paced visual category discovery

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING; SOFTWARE ENGINEERING;

EID: 80052880043     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995523     Document Type: Conference Paper
Times cited : (209)

References (23)
  • 2
    • 84927751020 scopus 로고    scopus 로고
    • From contours to regions: An empirical evaluation
    • P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. From Contours to Regions: An Empirical Evaluation. In CVPR, 2009.
    • (2009) CVPR
    • Arbelaez, P.1    Maire, M.2    Fowlkes, C.3    Malik, J.4
  • 4
    • 79851509694 scopus 로고    scopus 로고
    • Category independent object proposals
    • I. Endres and D. Hoiem. Category Independent Object Proposals. In ECCV, 2010.
    • (2010) ECCV
    • Endres, I.1    Hoiem, D.2
  • 5
    • 70449582001 scopus 로고    scopus 로고
    • Learning spatial context: Using stuff to find things
    • G. Heitz and D. Koller. Learning Spatial Context: Using Stuff to Find Things. In ECCV, 2008.
    • (2008) ECCV
    • Heitz, G.1    Koller, D.2
  • 6
    • 51949105707 scopus 로고    scopus 로고
    • Unsupervised modeling of object categories using link analysis techniques
    • G. Kim, C. Faloutsos, and M. Hebert. Unsupervised Modeling of Object Categories Using Link Analysis Techniques. In CVPR, 2008.
    • (2008) CVPR
    • Kim, G.1    Faloutsos, C.2    Hebert, M.3
  • 8
    • 85161967298 scopus 로고    scopus 로고
    • Self-paced learning for latent variable models
    • M. P. Kumar, B. Packer, and D. Koller. Self-Paced Learning for Latent Variable Models. In NIPS, 2010.
    • (2010) NIPS
    • Kumar, M.P.1    Packer, B.2    Koller, D.3
  • 9
    • 70450186103 scopus 로고    scopus 로고
    • An empirical bayes approach to contextual region classification
    • S. Lazebnik and M. Raginsky. An Empirical Bayes Approach to Contextual Region Classification. In CVPR, 2009.
    • (2009) CVPR
    • Lazebnik, S.1    Raginsky, M.2
  • 10
    • 68849114784 scopus 로고    scopus 로고
    • Foreground focus: Unsupervised learning from partially matching images
    • May
    • Y. J. Lee and K. Grauman. Foreground Focus: Unsupervised Learning from Partially Matching Images. IJCV, 85(2), May 2009.
    • (2009) IJCV , vol.85 , Issue.2
    • Lee, Y.J.1    Grauman, K.2
  • 11
    • 77955986573 scopus 로고    scopus 로고
    • Object-graphs for context-aware category discovery
    • Y. J. Lee and K. Grauman. Object-Graphs for Context-Aware Category Discovery. In CVPR, 2010.
    • (2010) CVPR
    • Lee, Y.J.1    Grauman, K.2
  • 12
    • 34948829141 scopus 로고    scopus 로고
    • OPTIMOL: Automatic object picture CollecTion via incremental model learning
    • L.-J. Li, G. Wang, and L. Fei-Fei. OPTIMOL: Automatic Object Picture CollecTion via Incremental mOdel Learning. In CVPR, 2007.
    • (2007) CVPR
    • Li, L.-J.1    Wang, G.2    Fei-Fei, L.3
  • 13
    • 50649084971 scopus 로고    scopus 로고
    • Unsupervised image categorization and object localization using topic models and correspondences between images
    • D. Liu and T. Chen. Unsupervised Image Categorization and Object Localization using Topic Models and Correspondences between Images. In ICCV, 2007.
    • (2007) ICCV
    • Liu, D.1    Chen, T.2
  • 15
    • 84858736606 scopus 로고    scopus 로고
    • Beyond categories: The visual memex model for reasoning about object relationships
    • T. Malisiewicz and A. Efros. Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships. In NIPS, 2009.
    • (2009) NIPS
    • Malisiewicz, T.1    Efros, A.2
  • 16
    • 53949084933 scopus 로고    scopus 로고
    • Single cluster graph partitioning for robotics applications
    • E. Olson, M. Walter, J. Leonard, and S. Teller. Single Cluster Graph Partitioning for Robotics Applications. In RSS, 2005.
    • (2005) RSS
    • Olson, E.1    Walter, M.2    Leonard, J.3    Teller, S.4
  • 17
    • 0000851002 scopus 로고    scopus 로고
    • A factorization approach to grouping
    • P. Perona and W. Freeman. A Factorization Approach to Grouping. In ECCV, 1998.
    • (1998) ECCV
    • Perona, P.1    Freeman, W.2
  • 18
    • 70049094447 scopus 로고    scopus 로고
    • Sparse feature learning for deep belief networks
    • M. Ranzato, Y. Boureau, and Y. LeCun. Sparse Feature Learning for Deep Belief Networks. In NIPS, 2007.
    • (2007) NIPS
    • Ranzato, M.1    Boureau, Y.2    LeCun, Y.3
  • 19
    • 33845596932 scopus 로고    scopus 로고
    • Using multiple segmentations to discover objects and their extent in image collections
    • B. Russell, A. Efros, J. Sivic, W. Freeman, and A. Zisserman. Using Multiple Segmentations to Discover Objects and their Extent in Image Collections. In CVPR, 2006.
    • (2006) CVPR
    • Russell, B.1    Efros, A.2    Sivic, J.3    Freeman, W.4    Zisserman, A.5
  • 20
    • 33845423382 scopus 로고    scopus 로고
    • Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation
    • J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation. In ECCV, 2006.
    • (2006) ECCV
    • Shotton, J.1    Winn, J.2    Rother, C.3    Criminisi, A.4
  • 22
    • 51949119486 scopus 로고    scopus 로고
    • Auto-context and application to high-level vision tasks
    • Z. Tu. Auto-context and Application to High-level Vision Tasks. In CVPR, 2008.
    • (2008) CVPR
    • Tu, Z.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.