-
1
-
-
84887347703
-
Tabula rasa: Model transfer for object category detection
-
1, 2, 3, 6, 7
-
Y. Aytar and A. Zisserman. Tabula rasa: Model transfer for object category detection. In CVPR, 2011. 1, 2, 3, 6, 7
-
(2011)
CVPR
-
-
Aytar, Y.1
Zisserman, A.2
-
2
-
-
85161970767
-
Exploiting weakly-labeled web images to improve object classification: A domain adaptation approach
-
2
-
A. Bergamo and L. Torresani. Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In NIPS, 2010. 2
-
(2010)
NIPS
-
-
Bergamo, A.1
Torresani, L.2
-
3
-
-
77953208851
-
Robust tracking-by-detection using a detector confidence particle filter
-
2
-
M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. V. Gool. Robust tracking-by-detection using a detector confidence particle filter. In ICCV, 2009. 2
-
(2009)
ICCV
-
-
Breitenstein, M.D.1
Reichlin, F.2
Leibe, B.3
Koller-Meier, E.4
Gool, L.V.5
-
4
-
-
80052418610
-
Multi-view learning in the presence of view disagreement
-
2
-
C. Christoudias, R. Urtasun, and T. Darrell. Multi-view learning in the presence of view disagreement. In UAI, 2008. 2
-
(2008)
UAI
-
-
Christoudias, C.1
Urtasun, R.2
Darrell, T.3
-
5
-
-
85057277997
-
Translated learning: Transfer learning across different feature spaces
-
2
-
W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu. Translated learning: Transfer learning across different feature spaces. In NIPS, 2008. 2
-
(2008)
NIPS
-
-
Dai, W.1
Chen, Y.2
Xue, G.-R.3
Yang, Q.4
Yu, Y.5
-
6
-
-
84860513476
-
Frustratingly easy domain adaptation
-
2
-
H. Daume III. Frustratingly easy domain adaptation. In ACL, 2007. 2
-
(2007)
ACL
-
-
Daume III, H.1
-
7
-
-
79953064532
-
Domain adaptation from multiple sources via auxiliary classifiers
-
1
-
L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua. Domain adaptation from multiple sources via auxiliary classifiers. In ICML, 2009. 1
-
(2009)
ICML
-
-
Duan, L.1
Tsang, I.W.2
Xu, D.3
Chua, T.-S.4
-
8
-
-
70450185098
-
Domain transfer SVM for video concept detection
-
2
-
L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank. Domain transfer SVM for video concept detection. In CVPR, 2009. 2
-
(2009)
CVPR
-
-
Duan, L.1
Tsang, I.W.2
Xu, D.3
Maybank, S.J.4
-
9
-
-
84867113087
-
Learning with augmented features for heterogeneous domain adaptation
-
1, 2
-
L. Duan, D. Xu, and I. Tsang. Learning with augmented features for heterogeneous domain adaptation. In ICML, 2012. 1, 2
-
(2012)
ICML
-
-
Duan, L.1
Xu, D.2
Tsang, I.3
-
10
-
-
77951298115
-
The pascal visual object classes (voc) challenge
-
6
-
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. IJCV, 88(2):303-338, 2010. 6
-
(2010)
IJCV
, vol.88
, Issue.2
, pp. 303-338
-
-
Everingham, M.1
Van Gool, L.2
Williams, C.3
Winn, J.4
Zisserman, A.5
-
11
-
-
77953224915
-
Learning to recognize activities from the wrong view point
-
2
-
A. Farhadi and M. K. Tabrizi. Learning to recognize activities from the wrong view point. In ECCV, 2008. 2
-
(2008)
ECCV
-
-
Farhadi, A.1
Tabrizi, M.K.2
-
12
-
-
77955422240
-
Object detection with discriminatively trained part based models
-
3, 4, 6, 7
-
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 32(9):1627-1645, 2010. 3, 4, 6, 7
-
(2010)
PAMI
, vol.32
, Issue.9
, pp. 1627-1645
-
-
Felzenszwalb, P.F.1
Girshick, R.B.2
McAllester, D.3
Ramanan, D.4
-
13
-
-
84866657270
-
Geodesic flow kernel for unsupervised domain adaptation
-
1, 2, 5
-
B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012. 1, 2, 5
-
(2012)
CVPR
-
-
Gong, B.1
Shi, Y.2
Sha, F.3
Grauman, K.4
-
14
-
-
84863396387
-
Domain adaptation for object recognition: An unsupervised approach
-
1, 2
-
R. Gopalan, R. Li, and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, 2011. 1, 2
-
(2011)
ICCV
-
-
Gopalan, R.1
Li, R.2
Chellappa, R.3
-
15
-
-
10044285992
-
Canonical correlation analysis: An overview with application to learning methods
-
2
-
D. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 16:26392664, 2004. 2
-
(2004)
Neural Computation
, vol.16
, pp. 2639-2664
-
-
Hardoon, D.1
Szedmak, S.2
Shawe-Taylor, J.3
-
16
-
-
85083950659
-
Efficient learning of domain-invariant image representations
-
1, 2, 3, 4, 7
-
J. Hoffman, E. Rodner, J. Donahue, T. Darrell, and K. Saenko. Efficient learning of domain-invariant image representations. In ICLR, 2013. 1, 2, 3, 4, 7
-
(2013)
ICLR
-
-
Hoffman, J.1
Rodner, E.2
Donahue, J.3
Darrell, T.4
Saenko, K.5
-
18
-
-
80052895155
-
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
-
1, 2, 5
-
B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011. 1, 2, 5
-
(2011)
CVPR
-
-
Kulis, B.1
Saenko, K.2
Darrell, T.3
-
19
-
-
84863045576
-
Key-segments for video object segmentation
-
1
-
Y. J. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In ICCV, 2011. 1
-
(2011)
ICCV
-
-
Lee, Y.J.1
Kim, J.2
Grauman, K.3
-
21
-
-
85088179929
-
A general model for multiple view unsupervised learning
-
2
-
B. Long, P. S. Yu, and Z. Zhang. A general model for multiple view unsupervised learning. In ICDM, 2009. 2
-
(2009)
ICDM
-
-
Long, B.1
Yu, P.S.2
Zhang, Z.3
-
22
-
-
79955855934
-
Laplacian support vector machines trained in the primal
-
2, 3
-
S. Melacci and M. Belkin. Laplacian support vector machines trained in the primal. JMLR, 12:1149-1184, 2011. 2, 3
-
(2011)
JMLR
, vol.12
, pp. 1149-1184
-
-
Melacci, S.1
Belkin, M.2
-
23
-
-
84866674032
-
Learning object class detectors from weakly annotated video
-
2, 4
-
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari. Learning object class detectors from weakly annotated video. In CVPR, 2012. 2, 4
-
(2012)
CVPR
-
-
Prest, A.1
Leistner, C.2
Civera, J.3
Schmid, C.4
Ferrari, V.5
-
24
-
-
78149301639
-
Adapting visual category models to new domains
-
1, 2
-
K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010. 1, 2
-
(2010)
ECCV
-
-
Saenko, K.1
Kulis, B.2
Fritz, M.3
Darrell, T.4
-
25
-
-
84866710736
-
-
Technical Report UCB/EECS-2012-209, EECS Department, University of California, Berkeley. 6
-
K. Saenko, B. Packer, C.-Y. Chen, S. Bandla, Y. Lee, Y. Jia, J.-C. Niebles, D. Koller, L. Fei-Fei, K. Grauman, and T. Darrell. Mid-level features improve recognition of interactive activities. Technical Report UCB/EECS-2012-209, EECS Department, University of California, Berkeley, 2012. 6
-
(2012)
Mid-level Features Improve Recognition of Interactive Activities
-
-
Saenko, K.1
Packer, B.2
Chen, C.-Y.3
Bandla, S.4
Lee, Y.5
Jia, Y.6
Niebles, J.-C.7
Koller, D.8
Fei-Fei, L.9
Grauman, K.10
Darrell, T.11
-
26
-
-
84867124651
-
Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
-
2
-
Y. Shi and F. Sha. Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. In ICML, 2012. 2
-
(2012)
ICML
-
-
Shi, Y.1
Sha, F.2
-
27
-
-
49549114434
-
Adapting SVM classifiers to data with shifted distributions
-
2
-
J. Yang, R. Yan, and A. Hauptmann. Adapting SVM classifiers to data with shifted distributions. In ICDM Workshops, 2007. 2
-
(2007)
ICDM Workshops
-
-
Yang, J.1
Yan, R.2
Hauptmann, A.3
-
28
-
-
37849026107
-
Cross-domain video concept detection using adaptive SVMs
-
1
-
J. Yang, R. Yan, and A. G. Hauptmann. Cross-domain video concept detection using adaptive SVMs. ACM Multimedia, 2007. 1
-
(2007)
ACM Multimedia
-
-
Yang, J.1
Yan, R.2
Hauptmann, A.G.3
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