Learning diverse image colorization

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 2877-2885

  Deshpande, Aditya ^a   Lu, Jiajun ^a   Yeh, Mao Chuang ^a   Chong, Min Jin ^a   Forsyth, David ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; LEARNING SYSTEMS;

ADVERSARIAL NETWORKS; AUTO ENCODERS; CONDITIONAL MODELS; GREY-LEVEL IMAGES; IMAGE COLORIZATIONS; LOW DIMENSIONAL EMBEDDING; MULTI-MODAL; PIXEL COLOR;

COMPUTER VISION;

EID: 85044377671     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.307     Document Type: Conference Paper

References (31)

1. D. Batra, P. Yadollahpour, A. Guzmn-Rivera, and G. Shakhnarovich. Diverse m-best solutions in markov random fields. In ECCV (5), volume 7576 of Lecture Notes in Computer Science, pages 1-16. Springer, 2012.
2. C. M. Bishop. Mixture density networks, 1994.
3. G. Charpiat, M. Hofmann, and B. Schölkopf. Automatic image colorization via multimodal predictions. In Proceedings of the 10th European Conference on Computer Vision: Part III, ECCV '08, pages 126-139, 2008.
4. Z. Cheng, Q. Yang, and B. Sheng. Deep colorization. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 415-423, Dec 2015.
5. A. Deshpande, J. Rock, and D. A. Forsyth. Learning largescale automatic image colorization. In ICCV, pages 567- 575. IEEE Computer Society, 2015.
6. K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra. Draw: A recurrent neural network for image generation. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 1462-1471, 2015.
7. G. B. Huang, M. Mattar, H. Lee, and E. Learned-Miller. Learning to align from scratch. In NIPS, 2012.
8. S. Iizuka, E. Simo-Serra, and H. Ishikawa. Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification. ACM Transactions on Graphics (Proc. of SIGGRAPH 2016), 35(4), 2016.
9. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167, 2015.
10. P. Isola, J. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In Computer Vision and Pattern Recognition, 2017.
11. J. Jancsary, S. Nowozin, and C. Rother. Loss-specific training of non-parametric image restoration models: A new state of the art. Proceedings of the 12th European Conference on Computer Vision - Volume Part VII, pages 112-125, 2012.
12. D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling. Semi-supervised learning with deep generative models. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3581-3589. 2014.
13. D. P. Kingma, T. Salimans, R. Jzefowicz, X. Chen, I. Sutskever, and M. Welling. Improving variational autoencoders with inverse autoregressive flow. In NIPS, pages 4736-4744, 2016.
14. D. P. Kingma and M. Welling. Auto-encoding variational bayes. International Conference on Learning Representations (ICLR), 2014.
15. T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum. Deep convolutional inverse graphics network. In Advances in Neural Information Processing Systems 28, pages 2539- 2547. 2015.
16. G. Larsson, M. Maire, and G. Shakhnarovich. Learning representations for automatic colorization. In European Conference on Computer Vision (ECCV), 2016.
17. E. Learned-Miller, G. B. Huang, A. RoyChowdhury, H. Li, and G. Hua. Labeled Faces in the Wild: A Survey, pages 189-248. Springer International Publishing, Cham, 2016.
18. A. Levin, D. Lischinski, and Y.Weiss. Colorization using optimization. ACM Trans. Graph., 23(3):689-694, Aug. 2004.
19. P. C. Mahalanobis. On Tests and Measures of Groups Divergence. International Journal of the Asiatic Society of Benagal, 26, 1930.
20. Y. Morimoto, Y. Taguchi, and T. Naemura. Automatic colorization of grayscale images using multiple images on the web. In SIGGRAPH 2009: Talks, SIGGRAPH '09, New York, NY, USA, 2009.
21. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434, 2015.
22. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
23. K. Sohn, X. Yan, and H. Lee. Learning structured output representation using deep conditional generative models. In Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS'15, pages 3483- 3491, Cambridge, MA, USA, 2015. MIT Press.
24. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15(1):1929- 1958, Jan. 2014.
25. J. Walker, C. Doersch, A. Gupta, and M. Hebert. An uncertain future: Forecasting from static images using variational autoencoders. In European Conference on Computer Vision, 2016.
26. T.Welsh, M. Ashikhmin, and K. Mueller. Transferring color to greyscale images. In SIGGRAPH, 2002.
27. T. Xue, J. Wu, K. L. Bouman, and W. T. Freeman. Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks. In NIPS, 2016.
28. X. Yan, J. Yang, K. Sohn, and H. Lee. Attribute2image: Conditional image generation from visual attributes. In Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV, pages 776-791, 2016.
29. F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. CoRR, abs/1506.03365, 2015.
30. R. Zhang, P. Isola, and A. A. Efros. Colorful image colorization. ECCV, 2016.
31. Y. Zhou and T. L. Berg. Learning Temporal Transformations from Time-Lapse Videos, pages 262-277. Springer International Publishing, 2016.