The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
Open accessAbstract
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controlling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthesizing photographic content with increased visual plausibility. Unlike standard MRF-based texture synthesis, the combined system can both match and adapt local features with considerable variability, yielding results far out of reach of classic generative MRF methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Research works printed but not linked: 24
- 10.1109/CVPR.2013.155 · DOI
- goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems I (NIPS), 2672
- he, 2012, Statistics of patch offsets for image completion, Proc European Conference on Computer Vision (ECCV), 16
- 10.1145/383259.383295 · DOI · Paper page
- 10.1145/1073204.1073263 · DOI · Paper page
- 10.1145/882262.882264 · DOI · Paper page
- 10.1145/1809939.1809945 · DOI
- 10.1145/1179352.1141921 · doi:10.1145/1179352.1141921
- 10.1145/2757287 · DOI
- lu, 2015, Learning FRAME models using CNN filters for knowledge visualization
- xu, 2014, Deep convolutional neural network for image deconvolution, Advances in Neural Information Processing Systems I (NIPS), 1790
- 10.1145/1306813.1306830 · DOI
- denton, 2015, Deep generative image models using a laplacian pyramid of adversarial networks, Advances in Neural Information Processing Systems (NIPS), 28
- efros, 1999, Texture synthesis by nonparametric sampling, Proc IEEE Int Conf Computer Vision (ICCV), 10.1109/ICCV.1999.790383 · DOI · Paper page
- 10.1007/978-3-319-10590-1_53 · DOI · Paper page
- 10.1145/383259.383296 · DOI · Paper page
- gatys, 2015, Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), 28
- gatys, 2015, A neural algorithm of artistic style
- barnes, 2009, Patchmatch: A randomized correspondence algorithm for structural image editing, ACM Trans Graph (Proc SIGGRAPH), 28, 24:1, 10.1145/1531326.1531330 · DOI · Paper page
- agarwala, 2004, Interactive digital photomontage, ACM Trans Graphics (Proc SIGGRAPH), 23, 294, 10.1145/1015706.1015718 · DOI · Paper page
- mordvintsev, 2015, Inceptionism Going deeper into neural networks
- simonyan, 2014, Very Deep Convolutional Networks for Large-scale Image Recognition
- 10.1145/344779.345009 · DOI · Paper page
- theis, 2015, Generative image modeling using spatial lstms, Advances in Neural Information Processing Systems I (NIPS)
Cited by
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076
Semantic Image Inpainting with Deep Generative Models
Yeh, R. A., Chen, C., Lim, T. Y., Schwing, A. G., Hasegawa-Johnson, M., & Do, M. N. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.728 · Cited by 1,206
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953
“In [22] and [40], GANs were employed to learn a mapping from one manifold to another for style transfer and inpainting, respectively.”
Method
Metadata
- Authors
- Li, C., & Wand, M.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2479-2486
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2275363859
- Cited by
- 741
- References
- 5
- FWCI
- 30.54
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
