메뉴 건너뛰기




Volumn , Issue , 2016, Pages 1270-1278

Local similarity-aware deep feature embedding

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTIONAL NETWORKS; FASTER CONVERGENCE; FEATURE SIMILARITIES; HIGH DENSITY REGIONS; INTER-CLASS DISTANCE; LOCAL NEIGHBORHOODS; POSITION DEPENDENTS; SIMILARITY METRICS;

EID: 85019222252     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (173)

References (37)
  • 1
    • 84947222450 scopus 로고    scopus 로고
    • Learning visual similarity for product design with convolutional neural networks
    • S. Bell and K. Bala. Learning visual similarity for product design with convolutional neural networks. TOG, 34(4):98:1-98:10, 2015.
    • (2015) TOG , vol.34 , Issue.4 , pp. 981-9810
    • Bell, S.1    Bala, K.2
  • 2
    • 84959186433 scopus 로고    scopus 로고
    • Towards open world recognition
    • A. Bendale and T. Boult. Towards open world recognition. In CVPR, 2015.
    • (2015) CVPR
    • Bendale, A.1    Boult, T.2
  • 3
    • 84986332657 scopus 로고    scopus 로고
    • Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop
    • Y. Cui, F. Zhou, Y. Lin, and S. Belongie. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In CVPR, 2016.
    • (2016) CVPR
    • Cui, Y.1    Zhou, F.2    Lin, Y.3    Belongie, S.4
  • 4
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 5
    • 80052876786 scopus 로고    scopus 로고
    • What does classifying more than 10, 000 image categories tell us?
    • J. Deng, A. C. Berg, K. Li, and L. Fei-Fei. What does classifying more than 10, 000 image categories tell us? In ECCV, 2010.
    • (2010) ECCV
    • Deng, J.1    Berg, A.C.2    Li, K.3    Fei-Fei, L.4
  • 6
    • 33144466753 scopus 로고    scopus 로고
    • One-shot learning of object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. TPAMI, 28(4):594-611, 2006.
    • (2006) TPAMI , vol.28 , Issue.4 , pp. 594-611
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 7
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part-based models
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32(9):1627-1645, 2010.
    • (2010) TPAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 9
    • 50649117726 scopus 로고    scopus 로고
    • Learning globally-consistent local distance functions for shape-based image retrieval and classification
    • A. Frome, Y. Singer, F. Sha, and J. Malik. Learning globally-consistent local distance functions for shape-based image retrieval and classification. In ICCV, 2007.
    • (2007) ICCV
    • Frome, A.1    Singer, Y.2    Sha, F.3    Malik, J.4
  • 10
    • 84940993365 scopus 로고    scopus 로고
    • Zero-shot object recognition by semantic manifold distance
    • Z. Fu, T. A. Xiang, E. Kodirov, and S. Gong. Zero-shot object recognition by semantic manifold distance. In CVPR, 2015.
    • (2015) CVPR
    • Fu, Z.1    Xiang, T.A.2    Kodirov, E.3    Gong, S.4
  • 12
    • 33845594193 scopus 로고    scopus 로고
    • Learning distance metrics with contextual constraints for image retrieval
    • S. C. H. Hoi, W. Liu, M. R. Lyu, and W.-Y. Ma. Learning distance metrics with contextual constraints for image retrieval. In CVPR, 2006.
    • (2006) CVPR
    • Hoi, S.C.H.1    Liu, W.2    Lyu, M.R.3    Ma, W.-Y.4
  • 13
    • 84986295253 scopus 로고    scopus 로고
    • Learning deep representation for imbalanced classification
    • C. Huang, Y. Li, C. C. Loy, and X. Tang. Learning deep representation for imbalanced classification. In CVPR, 2016.
    • (2016) CVPR
    • Huang, C.1    Li, Y.2    Loy, C.C.3    Tang, X.4
  • 14
    • 84986301038 scopus 로고    scopus 로고
    • Unsupervised learning of discriminative attributes and visual representations
    • C. Huang, C. C. Loy, and X. Tang. Unsupervised learning of discriminative attributes and visual representations. In CVPR, 2016.
    • (2016) CVPR
    • Huang, C.1    Loy, C.C.2    Tang, X.3
  • 15
    • 84897485170 scopus 로고    scopus 로고
    • 3D object representations for fine-grained categorization
    • J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3D object representations for fine-grained categorization. In ICCVW, 2013.
    • (2013) ICCVW
    • Krause, J.1    Stark, M.2    Deng, J.3    Fei-Fei, L.4
  • 16
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 17
    • 84894522762 scopus 로고    scopus 로고
    • Attribute-based classification for zero-shot visual object categorization
    • C. H. Lampert, H. Nickisch, and S. Harmeling. Attribute-based classification for zero-shot visual object categorization. TPAMI, 36(3):453-465, 2014.
    • (2014) TPAMI , vol.36 , Issue.3 , pp. 453-465
    • Lampert, C.H.1    Nickisch, H.2    Harmeling, S.3
  • 19
    • 57249084011 scopus 로고    scopus 로고
    • Visualizing high-dimensional data using t-SNE
    • L. Maaten and G. E. Hinton. Visualizing high-dimensional data using t-SNE. JMLR, 9:2579-2605, 2008.
    • (2008) JMLR , vol.9 , pp. 2579-2605
    • Maaten, L.1    Hinton, G.E.2
  • 20
    • 0035248924 scopus 로고    scopus 로고
    • PCA versus LDA
    • A. M. Martinez and A. C. Kak. PCA versus LDA. TPAMI, 23(2):228-233, 2001.
    • (2001) TPAMI , vol.23 , Issue.2 , pp. 228-233
    • Martinez, A.M.1    Kak, A.C.2
  • 21
    • 84884582871 scopus 로고    scopus 로고
    • Distance-based image classification: Generalizing to new classes at near-zero cost
    • T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. TPAMI, 35(11):2624-2637, 2013.
    • (2013) TPAMI , vol.35 , Issue.11 , pp. 2624-2637
    • Mensink, T.1    Verbeek, J.2    Perronnin, F.3    Csurka, G.4
  • 23
    • 84866652997 scopus 로고    scopus 로고
    • Towards good practice in large-scale learning for image classification
    • F. Perronnin, Z. Akata, Z. Harchaoui, and C. Schmid. Towards good practice in large-scale learning for image classification. In CVPR, 2012.
    • (2012) CVPR
    • Perronnin, F.1    Akata, Z.2    Harchaoui, Z.3    Schmid, C.4
  • 24
    • 84899001511 scopus 로고    scopus 로고
    • Transfer learning in a transductive setting
    • M. Rohrbach, S. Ebert, and B. Schiele. Transfer learning in a transductive setting. In NIPS, 2013.
    • (2013) NIPS
    • Rohrbach, M.1    Ebert, S.2    Schiele, B.3
  • 25
    • 80052892795 scopus 로고    scopus 로고
    • Evaluating knowledge transfer and zero-shot learning in a large-scale setting
    • M. Rohrbach, M. Stark, and B. Schiele. Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In CVPR, 2011.
    • (2011) CVPR
    • Rohrbach, M.1    Stark, M.2    Schiele, B.3
  • 26
    • 80052885179 scopus 로고    scopus 로고
    • High-dimensional signature compression for large-scale image classification
    • J. Sanchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In CVPR, 2011.
    • (2011) CVPR
    • Sanchez, J.1    Perronnin, F.2
  • 27
    • 84946751287 scopus 로고    scopus 로고
    • FaceNet: A unified embedding for face recognition and clustering
    • F. Schroff, D. Kalenichenko, and J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In CVPR, 2015.
    • (2015) CVPR
    • Schroff, F.1    Kalenichenko, D.2    Philbin, J.3
  • 29
    • 84986266755 scopus 로고    scopus 로고
    • Deep metric learning via lifted structured feature embedding
    • H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese. Deep metric learning via lifted structured feature embedding. In CVPR, 2016.
    • (2016) CVPR
    • Song, H.O.1    Xiang, Y.2    Jegelka, S.3    Savarese, S.4
  • 30
    • 84904163933 scopus 로고    scopus 로고
    • Dropout: A simple way to prevent neural networks from overfitting
    • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 15(1):1929-1958, 2014.
    • (2014) JMLR , vol.15 , Issue.1 , pp. 1929-1958
    • Srivastava, N.1    Hinton, G.2    Krizhevsky, A.3    Sutskever, I.4    Salakhutdinov, R.5
  • 34
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In ICCV, 2015.
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 35
    • 61749090884 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest neighbor classification
    • K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. JMLR, 10:207-244, 2009.
    • (2009) JMLR , vol.10 , pp. 207-244
    • Weinberger, K.Q.1    Saul, L.K.2
  • 36
    • 84879571292 scopus 로고    scopus 로고
    • Distance metric learning with application to clustering with side-information
    • E. P. Xing, M. I. Jordan, S. J. Russell, and A. Y. Ng. Distance metric learning with application to clustering with side-information. In NIPS, 2003.
    • (2003) NIPS
    • Xing, E.P.1    Jordan, M.I.2    Russell, S.J.3    Ng, A.Y.4
  • 37
    • 84866052108 scopus 로고    scopus 로고
    • Random forests for metric learning with implicit pairwise position dependence
    • C. Xiong, D. Johnson, R. Xu, and J. J. Corso. Random forests for metric learning with implicit pairwise position dependence. In SIGKDD, 2012.
    • (2012) SIGKDD
    • Xiong, C.1    Johnson, D.2    Xu, R.3    Corso, J.J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.