-
1
-
-
84911417279
-
Multiscale combinatorial grouping
-
4
-
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping. In CVPR, 2014.
-
(2014)
CVPR
-
-
Arbeláez, P.1
Pont-Tuset, J.2
Barron, J.T.3
Marques, F.4
Malik, J.5
-
2
-
-
84856675275
-
Tabula rasa: Model transfer for object category detection
-
2
-
Y. Aytar and A. Zisserman. Tabula rasa: Model transfer for object category detection. In ICCV, 2011.
-
(2011)
ICCV
-
-
Aytar, Y.1
Zisserman, A.2
-
3
-
-
84937961091
-
Do deep nets really need to be deep
-
Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors. Curran Associates, Inc.
-
J. Ba and R. Caruana. Do deep nets really need to be deep In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 2654-2662. Curran Associates, Inc., 2014.
-
(2014)
Advances in Neural Information Processing Systems
, vol.27
, pp. 2654-2662
-
-
Ba, J.1
Caruana, R.2
-
4
-
-
84906493570
-
Recognizing rgb images by learning from rgb-d data
-
2
-
L. Chen, W. Li, and D. Xu. Recognizing rgb images by learning from rgb-d data. In CVPR, 2014.
-
(2014)
CVPR
-
-
Chen, L.1
Li, W.2
Xu, D.3
-
6
-
-
85198028989
-
ImageNet: A large-scale hierarchical image database
-
1, 4, 5
-
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009.
-
(2009)
CVPR
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.-J.4
Li, K.5
Fei-Fei, L.6
-
7
-
-
84867113087
-
Learning with augmented features for heterogeneous domain adaptation
-
2
-
L. Duan, D. Xu, and I. W. Tsang. Learning with augmented features for heterogeneous domain adaptation. In ICML, 2012.
-
(2012)
ICML
-
-
Duan, L.1
Xu, D.2
Tsang, I.W.3
-
8
-
-
84973897611
-
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
-
5
-
D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015.
-
(2015)
ICCV
-
-
Eigen, D.1
Fergus, R.2
-
9
-
-
77951298115
-
The pascal visual object classes (voc) challenge
-
June 2010.
-
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88 (2): 303-338, June 2010.
-
International Journal of Computer Vision
, vol.88
, Issue.2
, pp. 303-338
-
-
Everingham, M.1
Van Gool, L.2
Williams, C.K.I.3
Winn, J.4
Zisserman, A.5
-
11
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
1
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
-
(2014)
CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
12
-
-
84866657270
-
Geodesic flow kernel for unsupervised domain adaptation
-
2
-
B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012.
-
(2012)
CVPR
-
-
Gong, B.1
Shi, Y.2
Sha, F.3
Grauman, K.4
-
15
-
-
84906344142
-
Learning rich features from rgb-d images for object detection and segmentation
-
Springer, 1, 2, 4, 5
-
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik. Learning rich features from rgb-d images for object detection and segmentation. In Computer Vision-ECCV 2014, pages 345-360. Springer, 2014.
-
(2014)
Computer Vision-ECCV 2014
, pp. 345-360
-
-
Gupta, S.1
Girshick, R.2
Arbeláez, P.3
Malik, J.4
-
16
-
-
84986258326
-
Cross modal distillation for supervision transfer
-
2, 3, 4, 5
-
S. Gupta, J. Hoffman, and J. Malik. Cross modal distillation for supervision transfer. In CVPR, 2016.
-
(2016)
CVPR
-
-
Gupta, S.1
Hoffman, J.2
Malik, J.3
-
18
-
-
85083950659
-
Efficient learning of domain-invariant image representations
-
2
-
J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell. Efficient learning of domain-invariant image representations. In ICLR, 2013.
-
(2013)
ICLR
-
-
Hoffman, J.1
Rodner, E.2
Donahue, J.3
Saenko, K.4
Darrell, T.5
-
19
-
-
84856668740
-
A category-level 3D object dataset: Putting the kinect to work
-
A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell. A category-level 3D object dataset: Putting the kinect to work. In Consumer Depth Cameras for Computer Vision. 2011.
-
(2011)
Consumer Depth Cameras for Computer Vision
, vol.2
-
-
Janoch, A.1
Karayev, S.2
Jia, Y.3
Barron, J.T.4
Fritz, M.5
Saenko, K.6
Darrell, T.7
-
20
-
-
85009867858
-
-
arXiv preprint arXiv: 1408. 5093, 4
-
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. ArXiv preprint arXiv: 1408. 5093, 2014.
-
(2014)
Caffe: Convolutional Architecture for Fast Feature Embedding
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
21
-
-
84876231242
-
ImageNet classification with deep convolutional neural networks
-
1, 4, 5
-
A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proc. NIPS, 2012.
-
(2012)
Proc. NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
22
-
-
80052895155
-
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
-
2
-
B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011.
-
(2011)
CVPR
-
-
Kulis, B.1
Saenko, K.2
Darrell, T.3
-
23
-
-
84959229072
-
Deep convolutional neural fields for depth estimation from a single image
-
5
-
F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In CVPR, 2015.
-
(2015)
CVPR
-
-
Liu, F.1
Shen, C.2
Lin, G.3
-
24
-
-
84959205572
-
Fully convolutional networks for semantic segmentation
-
1
-
J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
-
(2015)
CVPR
-
-
Long, J.1
Shelhamer, E.2
Darrell, T.3
-
25
-
-
84906347546
-
-
CoRR, abs/1312. 6229, 1
-
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312. 6229, 2013.
-
(2013)
Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks
-
-
Sermanet, P.1
Eigen, D.2
Zhang, X.3
Mathieu, M.4
Fergus, R.5
LeCun, Y.6
-
26
-
-
84898813471
-
Learning to rank using privileged information
-
Dec.
-
V. Sharmanska, N. Quadrianto, and C. Lampert. Learning to rank using privileged information. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 825-832, Dec 2013.
-
(2013)
Computer Vision (ICCV), 2013 IEEE International Conference on
, pp. 825-832
-
-
Sharmanska, V.1
Quadrianto, N.2
Lampert, C.3
-
27
-
-
84898771678
-
Building part-based object detectors via 3d geometry
-
2
-
A. Shrivastava and A. Gupta. Building part-based object detectors via 3d geometry. In ICCV, 2013.
-
(2013)
ICCV
-
-
Shrivastava, A.1
Gupta, A.2
-
29
-
-
84887331093
-
Accurate localization of 3D objects from RGB-D data using segmentation hypotheses
-
2
-
B. soo Kim, S. Xu, and S. Savarese. Accurate localization of 3D objects from RGB-D data using segmentation hypotheses. In CVPR, 2013.
-
(2013)
CVPR
-
-
Soo Kim, B.1
Xu, S.2
Savarese, S.3
-
30
-
-
84864429114
-
Leveraging rgb-d data: Adaptive fusion and domain adaptation for object detection
-
2
-
L. Spinello and K. O. Arras. Leveraging rgb-d data: Adaptive fusion and domain adaptation for object detection. In ICRA, 2012.
-
(2012)
ICRA
-
-
Spinello, L.1
Arras, K.O.2
-
31
-
-
85009879494
-
-
arXiv: 1409. 4842, 1
-
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. ArXiv: 1409. 4842, 2014.
-
(2014)
Going Deeper with Convolutions
-
-
Szegedy, C.1
Liu, W.2
Jia, Y.3
Sermanet, P.4
Reed, S.5
Anguelov, D.6
Erhan, D.7
Vanhoucke, V.8
Rabinovich, A.9
-
32
-
-
84887375121
-
Histogram of oriented normal vectors for object recognition with a depth sensor
-
2
-
S. Tang, X. Wang, X. Lv, T. X. Han, J. Keller, Z. He, M. Skubic, and S. Lao. Histogram of oriented normal vectors for object recognition with a depth sensor. In ACCV, 2012.
-
(2012)
ACCV
-
-
Tang, S.1
Wang, X.2
Lv, X.3
Han, T.X.4
Keller, J.5
He, Z.6
Skubic, M.7
Lao, S.8
-
34
-
-
68149165759
-
A new learning paradigm: Learning using privileged information
-
Advances in Neural Networks Research: {IJCNN20092009} International Joint Conference on Neural Networks.
-
V. Vapnik and A. Vashist. A new learning paradigm: Learning using privileged information. Neural Networks, 22 (56): 544-557, 2009. Advances in Neural Networks Research: {IJCNN20092009} International Joint Conference on Neural Networks.
-
(2009)
Neural Networks
, vol.22
, Issue.56
, pp. 544-557
-
-
Vapnik, V.1
Vashist, A.2
-
35
-
-
84946215753
-
Large-margin multi-modal deep learning for rgb-d object recognition
-
1, 2
-
A. Wang, J. Lu, J. Cai, T. Cham, and G. Wang. Large-margin multi-modal deep learning for rgb-d object recognition. In IEEE Transactions on Multimedia, 2015.
-
(2015)
IEEE Transactions on Multimedia
-
-
Wang, A.1
Lu, J.2
Cai, J.3
Cham, T.4
Wang, G.5
-
37
-
-
84973889989
-
Unsupervised learning of visual representations using videos
-
2
-
X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In ICCV, 2015.
-
(2015)
ICCV
-
-
Wang, X.1
Gupta, A.2
-
38
-
-
84894224170
-
-
Master's thesis, EECS Department, University of California, Berkeley, Jan.
-
E. S. Ye. Object detection in rgb-d indoor scenes. Master's thesis, EECS Department, University of California, Berkeley, Jan 2013.
-
(2013)
Object Detection in Rgb-d Indoor Scenes
-
-
Ye, E.S.1
-
39
-
-
84973861983
-
Conditional random fields as recurrent neural networks
-
1
-
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr. Conditional random fields as recurrent neural networks. In ICCV, 2015.
-
(2015)
ICCV
-
-
Zheng, S.1
Jayasumana, S.2
Romera-Paredes, B.3
Vineet, V.4
Su, Z.5
Du, D.6
Huang, C.7
Torr, P.8
|