Fetching the paper…
Reading the bibliography…
Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.
Horn, B.K.: Determining lightness from an image. Computer Graphics and Image Processing 3
1974
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255. IEEE (2009)
2009
Earlier work this paper cites.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10). pp. 807–814 (2010)
2010
Earlier work this paper cites.
Sundaram, N., Brox, T., Keutzer, K.: Dense point trajectories by gpu-accelerated large displacement optical flow. In: European Conference on Computer Vision. pp. 438–451. Springer (2010)
2010
Earlier work this paper cites.
Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: European Conference on Computer Vision. pp. 611–625. Springer (2012)
2012
Earlier work this paper cites.
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: Large displacement optical flow with deep matching. In: Computer Vision (ICCV), 2013 IEEE International Conference on. pp. 1385–1392. IEEE (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2758–2766 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2414–2423. IEEE (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stylebank: An explicit representation for neural image style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1897–1906 (2017)
2017
Later among the works it cites.
Gatys, L.A., Ecker, A.S., Bethge, M., Hertzmann, A., Shechtman, E.: Controlling perceptual factors in neural style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
Gupta, A., Johnson, J., Alahi, A., Fei-Fei, L.: Characterizing and improving stability in neural style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4067–4076 (2017)
2017
Later among the works it cites.
Huang, H., Wang, H., Luo, W., Ma, L., Jiang, W., Zhu, X., Li, Z., Liu, W.: Real-time neural style transfer for videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision. pp. 694–711. Springer (2016)
2016
Cited alongside, same era.
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4040–4048 (2016)
2016
Cited alongside, same era.
Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill (2016). https://doi.org/10.23915/distill.00003, http://distill.pub/2016/deconv-checkerboard
2016
Cited alongside, same era.
Ruder, M., Dosovitskiy, A., Brox, T.: Artistic style transfer for videos. In: German Conference on Pattern Recognition. pp. 26–36. Springer (2016)
2016
Cited alongside, same era.
Selim, A., Elgharib, M., Doyle, L.: Painting style transfer for head portraits using convolutional neural networks. ACM Transactions on Graphics (ToG) 35
2016
Cited alongside, same era.
Ulyanov, D., Lebedev, V., Vedaldi, A., Lempitsky, V.S.: Texture networks: Feed-forward synthesis of textures and stylized images. In: International Conference on Machine Learning. pp. 1349–1357 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Chen, D., Liao, J., Yuan, L., Yu, N., Hua, G.: Coherent online video style transfer. In: Proceedings of the IEEE International Conference on Computer Vision (Oct 2017)
2017
Cited alongside, same era.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. vol. 2 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6997–7005. IEEE (2017)
2017
Later among the works it cites.
Paszke, A., Chintala, S., Collobert, R., Kavukcuoglu, K., Farabet, C., Bengio, S., Melvin, I., Weston, J., Mariethoz, J.: Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration (2017)
2017
Later among the works it cites.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stereoscopic neural style transfer. CVPR 2018 (2018)
2018
Closest in time.
Ruder, M., Dosovitskiy, A., Brox, T.: Artistic style transfer for videos and spherical images. International Journal of Computer Vision pp. 1–21 (2018)
2018
Closest in time.
Videvo: Videvo free footage (2018), https://www.videvo.net/ , [Online at https://www.videvo.net/; accessed 26-February-2018]
2018
Closest in time.