The DOI matches its Crossref record; 48 of the 51 references it cites were found in a registry or an index.
Learning Representations for Automatic Colorization
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The auxiliary pretext is primarily used to fill in missing parts of an image [6], convert it to gray scale [7], predict hidden parts, and many other tasks.”
From Self-supervised learning: A succinct review (2023) · cites this paper as [7]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,935
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
(2010) · Cited by 14,134
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,417
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,401
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Colorization using optimization
Levin, A., Lischinski, D., & Weiss, Y. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015780 · Cited by 1,446
Iizuka, S., Simo‐Serra, E., & Ishikawa, H. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925974 · Cited by 830
Bronstein, A. M., Bronstein, M. M., Guibas, L. J., & Ovsjanikov, M. (2011) · ACM Transactions on Graphics · doi:10.1145/1899404.1899405 · Cited by 811
Transferring color to greyscale images
Welsh, T. F., Ashikhmin, M., & Mueller, K. D. (2002) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/566570.566576 · Cited by 729
Cheng, Z., Yang, Q., & Sheng, B. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.55 · Cited by 614
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299024 · Cited by 554
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
Bertasius, G., Shi, J., & Torresani, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299067 · Cited by 549
Matching Real Fabrics with Micro-Appearance Models
Khungurn, P., Schroeder, D., Zhao, S., Bala, K., & Marschner, S. (2015) · ACM Transactions on Graphics · doi:10.1145/2818648 · Cited by 539
Transferring color to greyscale images
Welsh, T. F., Ashikhmin, M., & Mueller, K. D. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566576 · Cited by 537
Fast image and video colorization using chrominance blending
Yatziv, L., & Sapiro, G. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.864231 · Cited by 534
A fast local descriptor for dense matching
Tola, E., Lepetit, V., & Fua, P. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587673 · Cited by 507
Feedforward semantic segmentation with zoom-out features
Mostajabi, M., Yadollahpour, P., & Shakhnarovich, G. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298959 · Cited by 446
Self-citationThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding
Patterson, G., Xu, C., Su, H., & Hays, J. H. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-013-0695-z · Cited by 398
Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization
Tai, Y., Jia, J., & Tang, C. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.215 · Cited by 341
Qu, Y., Wong, T., & Heng, P. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1142017 · Cited by 297
Image colorization using similar images
Gupta, R. K., Chia, A. Y.-S., Rajan, D., Ng, E. S., & Zhiyong, H. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393402 · Cited by 271
$$N^4$$-Fields: Neural Network Nearest Neighbor Fields for Image Transforms
Ganin, Y., & Lempitsky, V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_36 · Cited by 262
Ironi, R., Cohen‐Or, D., & Lischinski, D. (2005) · Eurographics · doi:10.2312/egwr/egsr05/201-210 · Cited by 255
Automatic Image Colorization Via Multimodal Predictions
Charpiat, G., Hofmann, M., & Schölkopf, B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_10 · Cited by 239
Luan, Q., Wen, F., Cohen‐Or, D., Liang, L., Xu, Y.-Q., & Shum, H. (2007) · doi:10.2312/egwr/egsr07/309-320 · Cited by 236
An adaptive edge detection based colorization algorithm and its applications
Huang, Y., Tung, Y.-S., Chen, J.-C., Wang, S.-W., & Wu, J. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101223 · Cited by 234
Learning Large-Scale Automatic Image Colorization
Deshpande, A., Rock, J., & Forsyth, D. A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.72 · Cited by 215
Semantic colorization with internet images
Chia, A. Y.-S., Zhuo, S., Gupta, R. K., Tai, Y., Cho, S., Tan, P., & Lin, S. C.-F. (2011) · ACM Transactions on Graphics · doi:10.1145/2070781.2024190 · Cited by 183
Semantic colorization with internet images
Chia, A. Y.-S., Zhuo, S., Gupta, R. K., Tai, Y., Cho, S., Tan, P., & Lin, S. C.-F. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2024156.2024190 · Cited by 167
Unsupervised colorization of black-and-white cartoons
Sýkora, D., Buriánek, J. D., & Žára, J. (2004) · International Symposium on Non-Photorealistic Animation and Rendering (NPAR) · doi:10.1145/987657.987677 · Cited by 110
Sapiro, G. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530151 · Cited by 60
Automatic colorization of grayscale images using multiple images on the web
Morimoto, Y., Taguchi, Y., & Naemura, T. (2009) · doi:10.1145/1597990.1598049 · Cited by 49
Book Review: Computational Photography: Methods and Applications, by Rastislav Lukac, Ed.
Radha, H. (2011) · Journal of Electronic Imaging · doi:10.1117/1.3663232 · Cited by 39
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
Maire, M., Yu, S. X., & Perona, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16817-3_18 · Cited by 35
Self-citationTsaftaris, S. A., Casadio, F., Andral, J.-L., & Katsaggelos, A. K. (2013) · Studies in Conservation · doi:10.1179/2047058413y.0000000104 · Cited by 21
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
Ion, A., Carreira, J., & Sminchisescu, C. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0663-7 · Cited by 19
Automatic colorization of grayscale images using multiple images on the web
Morimoto, Y., Taguchi, Y., & Naemura, T. (2009) · doi:10.1145/1599301.1599333 · Cited by 17
Machine Learning Methods for Automatic Image Colorization
Charpiat, G., Bezrukov, I., Hofmann, M., Altun, Y., Schölkopf, B., & Lukáč, R. (2010) · Cited by 9
Research works printed but not linked: 3
- Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning (2016). arXiv preprint arXiv:1605.09782 · arXiv
- Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093 · arXiv
- Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: looking wider to see better (2015). arXiv preprint arXiv:1506.04579 · arXiv
Cited by
Sorted by citations
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
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846
Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,417
Deep Clustering for Unsupervised Learning of Visual Features
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674
Metadata
- Authors
- Larsson, G., Maire, M., & Shakhnarovich, G.
- Affiliations
- University of Chicago (US)
- Toyota Technological Institute at Chicago (US)
- Corresponding author
- Larsson, Gustav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 577-593
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2308529009
- Cited by
- 946
- References
- 51
- Citations by year
- 2026: 25 · 2025: 45 · 2024: 75 · 2023: 109 · 2022: 179 · 2021: 176 · 2020: 133 · 2019: 93 · 2018: 63 · 2017: 43 · 2016: 5
- FWCI
- 23.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image colorization, automatic colorization, self-supervised visual representation learning, semantic representation
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