WarpNet: Weakly Supervised Matching for Single-View Reconstruction

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 3253-3261

  Kanazawa, Angjoo ^a   Jacobs, David W ^a   Chandraker, Manmohan ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; PATTERN RECOGNITION;

ANNOTATED POINTS; ARTIFICIAL DATA; DEEP LEARNING; DISCRIMINATIVE LEARNING; FINE GRAINED; POSE VARIATION; SINGLE-VIEW RECONSTRUCTION; SPATIAL PRIORS;

IMAGE MATCHING;

EID: 84986301983     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.354     Document Type: Conference Paper

References (34)

1. A.Dosovitskiy, J.T.Springenberg, M.Riedmiller, and T.Brox. Discriminative unsupervised feature learning with convolutional neural networks. In Advances in Neural Information Processing Systems 27 (NIPS), 2014.
2. P. Agrawal, J. Carreira, and J. Malik. Learning to see by moving. In ICCV, 2015.
3. S. Y.-Z. Bao, M. Chandraker, Y. Lin, and S. Savarese. Dense object reconstruction with semantic priors. In CVPR. IEEE, 2013.
4. P. N. Belhumeur, D.W. Jacobs, D. J. Kriegman, and N. Kumar. Localizing parts of faces using a consensus of exemplars. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(12):2930-2940, 2013.
5. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(4):509-522, 2002.
6. T. Berg and P. N. Belhumeur. How do you tell a blackbird from a crow? In Proc. Int. Conf. Computer Vision (ICCV), December 2013.
7. J. Carreira, A. Kar, S. Tulsiani, and J. Malik. Virtual view networks for object reconstruction. In CVPR. IEEE, 2015.
8. T. J. Cashman and A. W. Fitzgibbon. What shape are dolphins? building 3D morphable models from 2D images. IEEE Trans. Pattern Anal. Mach. Intell, 35(1):232-244, 2013.
9. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In British Machine Vision Conference, 2014.
10. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In ICLR, 2015.
11. S. Chopra, R. Hadsell, and Y. L. L. Cun. Learning a similarity metric discriminatively, with application to face verification. In CVPR, pages I: 539-546, 2005.
12. P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Haz?rbas?, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. ICCV, 2015.
13. X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg. Matchnet: Unifying feature and metric learning for patchbased matching. In CVPR. IEEE, 2015.
14. M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu. Spatial transformer networks. 2015.
15. A. Kar, S. Tulsiani, J. Carreira, and J. Malik. Categoryspecific object reconstruction from a single image. 2015.
16. J. Kim, C. Liu, F. Sha, and K. Grauman. Deformable spatial pyramid matching for fast dense correspondences. In CVPR. IEEE, 2013.
17. J. Krause, H. Jin, J. Yang, and F.-F. Li. Fine-grained recognition without part annotations. In CVPR, pages 5546-5555. IEEE, 2015.
18. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
19. J. L. Long, N. Zhang, and T. Darrell. Do convnets learn correspondence? In Advances in Neural Information Processing Systems, pages 1601-1609, 2014.
20. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004.
21. M. Marques and J. P. Costeira. Estimating 3D shape from degenerate sequences with missing data. Computer Vision and Image Understanding, pages 261-272, Feb. 2009.
22. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630, 2005.
23. C. L. of Ornithology. The birds of north america online (p. rodewald, ed.). http://bna.birds.cornell.edu/BNA/, August 2015.
24. N. Snavely, S. M. Seitz, and R. Szeliski. Modeling the world from Internet photo collections. IJCV, November 2008.
25. N. Snavely, S. M. Seitz, and R. Szeliski. Skeletal graphs for efficient structure from motion. In Proc. Computer Vision and Pattern Recognition, 2008.
26. J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Advances in Neural Information Processing Systems, pages 1799-1807, 2014.
27. Y. Tsuduki and H. Fujiyoshi. A method for visualizing pedestrian traffic flow using SIFT feature point tracking. In PSIVT, pages 25-36, 2009.
28. S. Tulsiani and J. Malik. Viewpoints and keypoints. In Computer Vision and Pattern Regognition (CVPR), 2015.
29. S. Vicente, J. Carreira, L. de Agapito, and J. Batista. Reconstructing PASCAL VOC. In CVPR. IEEE, 2014.
30. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.
31. S. Winder, G. Hua, and M. Brown. Picking the best DAISY. In CVPR, pages 178-185, 2009.
32. Y. Yang and D. Ramanan. Articulated pose estimation with flexible mixtures-of-parts. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1385-1392. IEEE, 2011.
33. S. Zagoruyko and N. Komodakis. Learning to compare image patches via convolutional neural networks. CVPR, 2015.
34. T. Zhou, Y. J. Lee, S. X. Yu, and A. A. Efros. Flowweb: Joint image set alignment by weaving consistent, pixel-wise correspondences. In CVPR. IEEE, 2015.