-
1
-
-
84973916088
-
Unsupervised visual representation learning by context prediction
-
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422-1430 (2015)
-
(2015)
Proceedings of the IEEE International Conference on Computer Vision
, pp. 1422-1430
-
-
Doersch, C.1
Gupta, A.2
Efros, A.A.3
-
5
-
-
84937849144
-
Generative adversarial nets
-
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672-2680 (2014)
-
(2014)
Advances in Neural Information Processing Systems
, pp. 2672-2680
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
6
-
-
84965143571
-
Deep generative image models using Laplacian pyramid of adversarial networks
-
Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486-1494 (2015)
-
(2015)
Advances in Neural Information Processing Systems
, pp. 1486-1494
-
-
Denton, E.L.1
Chintala, S.2
Fergus, R.3
-
9
-
-
84864073449
-
Greedy layer-wise training of deep networks
-
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19, 153 (2007)
-
(2007)
Adv. Neural Inf. Process. Syst.
, vol.19
, pp. 153
-
-
Bengio, Y.1
Lamblin, P.2
Popovici, D.3
Larochelle, H.4
-
10
-
-
84990022453
-
Generative image modeling using style and structure adversarial networks
-
Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. In: ECCV (2016)
-
(2016)
ECCV
-
-
Wang, X.1
Gupta, A.2
-
11
-
-
84973926501
-
Learning to see by moving
-
Agrawal, P., Carreira, J., Malik, J.: Learning to see by moving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 37-45 (2015)
-
(2015)
Proceedings of the IEEE International Conference on Computer Vision
, pp. 37-45
-
-
Agrawal, P.1
Carreira, J.2
Malik, J.3
-
12
-
-
71149084945
-
Deep learning from temporal coherence in video
-
ACM
-
Mobahi, H., Collobert, R., Weston, J.: Deep learning from temporal coherence in video. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 737-744. ACM (2009)
-
(2009)
Proceedings of the 26th Annual International Conference on Machine Learning
, pp. 737-744
-
-
Mobahi, H.1
Collobert, R.2
Weston, J.3
-
13
-
-
84937943470
-
Depth map prediction from a single image using a multi-scale deep network
-
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in neural information processing systems, pp. 2366-2374 (2014)
-
(2014)
Advances in neural information processing systems
, pp. 2366-2374
-
-
Eigen, D.1
Puhrsch, C.2
Fergus, R.3
-
14
-
-
84959234840
-
Designing deep networks for surface normal estimation
-
Wang, X., Fouhey, D., Gupta, A.: Designing deep networks for surface normal estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 539-547 (2015)
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 539-547
-
-
Wang, X.1
Fouhey, D.2
Gupta, A.3
-
15
-
-
84973880490
-
Dense optical flow prediction from a static image
-
Walker, J., Gupta, A., Hebert, M.: Dense optical flow prediction from a static image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2443-2451 (2015)
-
(2015)
Proceedings of the IEEE International Conference on Computer Vision
, pp. 2443-2451
-
-
Walker, J.1
Gupta, A.2
Hebert, M.3
-
16
-
-
0141629808
-
Movement-produced stimulation in the development of visually guided behavior
-
Held, R., Hein, A.: Movement-produced stimulation in the development of visually guided behavior. J. Comp. Physiol. Psychol. 56(5), 872 (1963)
-
(1963)
J. Comp. Physiol. Psychol.
, vol.56
, Issue.5
, pp. 872
-
-
Held, R.1
Hein, A.2
-
17
-
-
0033724358
-
Robotic grasping and contact: A review
-
Citeseer
-
Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: ICRA, pp. 348-353, Citeseer (2000)
-
(2000)
ICRA
, pp. 348-353
-
-
Bicchi, A.1
Kumar, V.2
-
18
-
-
84898489752
-
Data-driven grasp synthesis a survey
-
Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis a survey. IEEE Trans. Robot. 30(2), 289-309 (2014)
-
(2014)
IEEE Trans. Robot.
, vol.30
, Issue.2
, pp. 289-309
-
-
Bohg, J.1
Morales, A.2
Asfour, T.3
Kragic, D.4
-
20
-
-
84977591425
-
Using experience for assessing grasp reliability
-
Morales, A., Chinellato, E., Fagg, A.H., Del Pobil, A.P.: Using experience for assessing grasp reliability. In: IJRR
-
IJRR
-
-
Morales, A.1
Chinellato, E.2
Fagg, A.H.3
Del Pobil, A.P.4
-
21
-
-
71049163738
-
Learning object-specific grasp affordance densities
-
Detry, R., Baseski, E., Popovic, M., Touati, Y., Kruger, N., Kroemer, O., Peters, J., Piater, J.: Learning object-specific grasp affordance densities. In: ICDL (2009)
-
(2009)
ICDL
-
-
Detry, R.1
Baseski, E.2
Popovic, M.3
Touati, Y.4
Kruger, N.5
Kroemer, O.6
Peters, J.7
Piater, J.8
-
22
-
-
84900436097
-
A data-driven statistical framework for post-grasp manipulation
-
Paolini, R., Rodriguez, A., Srinivasa, S., Mason, M.T.: A data-driven statistical framework for post-grasp manipulation. IJRR 33(4), 600-615 (2014)
-
(2014)
IJRR
, vol.33
, Issue.4
, pp. 600-615
-
-
Paolini, R.1
Rodriguez, A.2
Srinivasa, S.3
Mason, M.T.4
-
24
-
-
84979224293
-
-
arXiv preprint arXiv: 1603.02199
-
Levine, S., Pastor, P., Krizhevsky, A., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016). arXiv preprint arXiv: 1603.02199
-
(2016)
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
-
-
Levine, S.1
Pastor, P.2
Krizhevsky, A.3
Quillen, D.4
-
27
-
-
0000661972
-
Stable pushing: Mechanics, controllability, and planning
-
Lynch, K.M., Mason, M.T.: Stable pushing: mechanics, controllability, and planning. Int. J. Robot. Res. 15(6), 533-556 (1996)
-
(1996)
Int. J. Robot. Res.
, vol.15
, Issue.6
, pp. 533-556
-
-
Lynch, K.M.1
Mason, M.T.2
-
29
-
-
0027541190
-
Object handling using two arms without grasping
-
Yun, X.: Object handling using two arms without grasping. Int. J. Robot. Res. 12(1), 99-106 (1993)
-
(1993)
Int. J. Robot. Res.
, vol.12
, Issue.1
, pp. 99-106
-
-
Yun, X.1
-
30
-
-
84977536806
-
-
Zhou, J., Paolini, R., Bagnell, J.A., Mason, M.T.: A convex polynomial forcemotion model for planar sliding: Identification and application (2016)
-
(2016)
A convex polynomial forcemotion model for planar sliding: Identification and application
-
-
Zhou, J.1
Paolini, R.2
Bagnell, J.A.3
Mason, M.T.4
-
31
-
-
72149125598
-
Exoskeletal force-sensing end-effectors with embedded optical fiber-bragg-grating sensors
-
Park, Y.L., Ryu, S.C., Black, R.J., Chau, K.K., Moslehi, B., Cutkosky, M.R.: Exoskeletal force-sensing end-effectors with embedded optical fiber-bragg-grating sensors. IEEE Trans. Robot. 25(6), 1319-1331 (2009)
-
(2009)
IEEE Trans. Robot.
, vol.25
, Issue.6
, pp. 1319-1331
-
-
Park, Y.L.1
Ryu, S.C.2
Black, R.J.3
Chau, K.K.4
Moslehi, B.5
Cutkosky, M.R.6
-
32
-
-
76249085561
-
Object identification with tactile sensors using bag-of-features
-
IEEE
-
Schneider, A., Sturm, J., Stachniss, C., Reisert, M., Burkhardt, H., Burgard, W.: Object identification with tactile sensors using bag-of-features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 243-248. IEEE (2009)
-
(2009)
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
, pp. 243-248
-
-
Schneider, A.1
Sturm, J.2
Stachniss, C.3
Reisert, M.4
Burkhardt, H.5
Burgard, W.6
-
33
-
-
34250088854
-
Active vision
-
Aloimonos, J., Weiss, I., Bandyopadhyay, A.: Active vision. Int. J. Comput. Vis. 1(4), 333-356 (1988)
-
(1988)
Int. J. Comput. Vis.
, vol.1
, Issue.4
, pp. 333-356
-
-
Aloimonos, J.1
Weiss, I.2
Bandyopadhyay, A.3
-
34
-
-
84949636429
-
3D shapenets: A deep representation for volumetric shapes
-
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912-1920 (2015)
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 1912-1920
-
-
Wu, Z.1
Song, S.2
Khosla, A.3
Yu, F.4
Zhang, L.5
Tang, X.6
Xiao, J.7
-
35
-
-
84990871919
-
-
Mahler, J., Pokorny, F.T., Hou, B., Roderick, M., Laskey, M., Aubry, M., Kohlhoff, K., Kroeger, T., Kuffner, J., Goldberg, K.: Dex-Net 1.0: a cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards
-
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards
-
-
Mahler, J.1
Pokorny, F.T.2
Hou, B.3
Roderick, M.4
Laskey, M.5
Aubry, M.6
Kohlhoff, K.7
Kroeger, T.8
Kuffner, J.9
Goldberg, K.10
-
38
-
-
84990841560
-
-
arXiv preprint arXiv: 1511.07111
-
Tzeng, E., Devin, C., Hoffman, J., Finn, C., Peng, X., Levine, S., Saenko, K., Darrell, T.: Towards adapting deep visuomotor representations from simulated to real environments (2015). arXiv preprint arXiv: 1511.07111
-
(2015)
Towards adapting deep visuomotor representations from simulated to real environments
-
-
Tzeng, E.1
Devin, C.2
Hoffman, J.3
Finn, C.4
Peng, X.5
Levine, S.6
Saenko, K.7
Darrell, T.8
-
39
-
-
84990851833
-
Deep spatial autoencoders for visuomotor learning
-
Finn, C., Tan, X.Y., Duan, Y., Darrell, T., Levine, S., Abbeel, P.: Deep spatial autoencoders for visuomotor learning. Reconstruction 117(117), 240 (2015)
-
(2015)
Reconstruction
, vol.117
, Issue.117
, pp. 240
-
-
Finn, C.1
Tan, X.Y.2
Duan, Y.3
Darrell, T.4
Levine, S.5
Abbeel, P.6
-
40
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097-1105 (2012)
-
(2012)
NIPS
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
41
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
IEEE
-
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248-255. IEEE (2009)
-
(2009)
CVPR 2009
, pp. 248-255
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.J.4
Li, K.5
Fei-Fei, L.6
-
42
-
-
84455168545
-
A large-scale hierarchical multi-view RGB-D object dataset
-
IEEE
-
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1817-1824. IEEE (2011)
-
(2011)
2011 IEEE International Conference on Robotics and Automation (ICRA)
, pp. 1817-1824
-
-
Lai, K.1
Bo, L.2
Ren, X.3
Fox, D.4
|