Cited by 946Image Enhancement TechniquesGenerative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

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

Larsson, Gustav, Maire, Michael, Shakhnarovich, Gregory·2016·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-46493-0_35

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-3-319-46493-0_35

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
Untitled work
(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
Colorful Image Colorization
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

Show all 51 references

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

Sorted by citations

Items 801–900 of 946

Referenced Based Color Transfer for Medical Volume Rendering
Devkota, S., & Pattanaik, S. N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64556-4_16 · Cited by 1
See Fine Color from the Rough Black-and-White
Wu, J., Ning, L., & Zhou, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69244-5_20 · Cited by 1
Fusion of handcrafted edge and residual learning features for image colorization
Deshpande, S. C., Pawer, M. M., Atkale, D. V., & Yadav, D. M. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-01871-z · Cited by 1
Improvement of Message Processing-based Steganographic Algorithm using Convolutional Neural Networks
Găină, L. I., Gușiță, B., & Stănescu, D. (2021) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc52150.2021.9606860 · Cited by 1
Qualitative Colorization of Thermal Infrared Images using custom Convolutional Neural Networks
Dangle, A., Gore, S. A., Mundada, R., Shrungare, J., & Dalal, H. (2022) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon54057.2022.9752847 · Cited by 1
Improving Image Clustering through Sample Ranking and Its Application to Remote Sensing Images
Li, Q., & Qiu, G. (2022) · Remote Sensing · doi:10.3390/rs14143317 · Cited by 2
On the robustness of self-supervised representations for multi-view object classification
Torpey, D., & Klein, R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.07.016 · Cited by 1
Image Colorization with Dense Feature Fusion
Sun, L., & Shi, K. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9855935 · Cited by 1
View Sub-sampling and Reconstruction for Efficient Light Field Compression
Chen, Y., Alain, M., & Smolić, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/swcc3620 · Cited by 1
On the Feasibility of Privacy-Secured Facial Authentication for low-power IoT Devices - Quantifying the Effects of Head Pose Variation on End-to-End Neural Face Recognition
Yao, W., Varkarakis, V., Lemley, J., & Corcoran, P. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/fevr2516 · Cited by 1
A machine vision system for avian song classification with CNN’s
Palma, G., Aquino, A., Monticelli, P., Verdade, L. M., Markham, C., & Moral, R. D. A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/mhzn4111 · Cited by 1
Reality Analagous Synthetic Dataset Generation with Daylight Variance for Deep Learning Classification
Lee, T., McKeever, S., & Courtney, J. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/poya9239 · Cited by 1
Dual spatial constraints-based few-shot image classification
Zhu, S., Bian, X., & Liang, Z. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063029 · Cited by 1
Multi-level Self-supervised Representation Learning via Triple-way Attention Fusion and Local Similarity Optimization
Zhang, S., Chen, F., Zhang, J., Liu, A., & Wang, F. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11110-2 · Cited by 1
Saliency Can Be All You Need in Contrastive Self-supervised Learning
Kocaman, V., Shir, O. M., Bäck, T., & Belbachir, A. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20716-7_10 · Cited by 1
Space-correlated Contrastive Representation Learning with Multiple Instances
Song, D., Gao, Y., Yan, J., Sun, W., & Zheng, W. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956034 · Cited by 1
PCMask: A Dual-Branch Self-supervised Medical Image Segmentation Method Using Pixel-Level Contrastive Learning and Masked Image Modeling
Wang, Y., Liu, B., & Zhou, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25825-1_36 · Cited by 1
Image Colorization: A Convolutional Network Approach
Pradhan, N., Gupta, S., & Srivastava, G. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6634-7_37 · Cited by 1
Deep Image Prior Regularized by Coupled Total Variation for Image Colorization
Agazzotti, G., Pierre, F., & Sur, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-31975-4_23 · Cited by 1
Siamese Representation Learning for Unsupervised Relation Extraction
Zhang, G., & Chen, S. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230616 · Cited by 1
Deep Transfer Learning (DTL) Based-Framework for an Accurate Multi-classification of MRI Brain Tumors
Mzoughi, H., Njeh, I., Slima, M. B., Farhat, N., & Mhiri, C. (2023) · International Conference on Cyberworlds (CW) · doi:10.1109/cw58918.2023.00022 · Cited by 1
Generation of PAS-Stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method
Peng, J., Chen, G., Saruta, K., & Terata, Y. (2024) · Imaging and Radiation Research · doi:10.24294/irr.v6i1.4085 · Cited by 1
Using colorization as a tool for automatic makeup suggestionPreprint
Gowda, S. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07421 · Cited by 1
Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based ApproachPreprint
Pan, Z., Yuan, F., Lei, J., & Kwong, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10653 · Cited by 1
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational AutoencodersPreprint
Akbarinia, A., Rodríguez, R. G., Flachot, A., & Toscani, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.14487 · Cited by 1
Deep auxiliary learning for visual localization using colorization taskPreprint
Tian, M., Nie, Q., Shen, H., & Xia, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00222 · Cited by 1
A Deep Dive into Deep ClusterPreprint
Mustapha, A., Khreich, W., & Masri, W. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4173678 · Cited by 1
Audiovisual speech representation learning applied to emotion recognition
Sadok, S. (2024) · theses.fr (ABES) · doi:10.70675/0f15a36fzecf8z4cffz94e7z120e7d8fd39d · Cited by 1
Image Colorization Based on Locally Linear Embedding
Aiyoshizawa, T., Konishi, K., Uruma, K., Sasaki, R., Takahashi, T., & Furukawa, T. (2018) · Journal of Signal Processing · doi:10.2299/jsp.22.203
Learning to see across Domains and ModalitiesPreprint
Carlucci, F. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04992
ChromaGAN: An Adversarial Approach for Picture Colorization.Preprint
Vitoria, P., Raad, L., & Ballester, C. (2019) · arXiv (Cornell University)
Semantic Deep Intermodal Feature Transfer: Transferring Feature Descriptors Between Imaging ModalitiesPreprint
Kleinschmidt, S. P., & Wagner, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11436
Learning to Invert Pseudo-Spectral Data for Seismic Waveform Inversion
Zerafa, C., Galea, P., & Sebu, C. (2019) · OAR@UM (University of Malta) · doi:10.48550/arxiv.1911.09549
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly DetectionPreprint
Muaz, U., & Sobolevsky, S. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02864
Automatic colorization of digital halftone images using neural networks
Serdyuk, M. E., Syryk, S. F., & Sokol, O. (2019) · Problems of applied mathematics and mathematic modeling · doi:10.15421/321915
Computational photography for efficient image and video acquisition
Zhang, B. (2019) · doi:10.14711/thesis-991012717868003412
An End-to-end System for Pests and Diseases Identification
Zhang, N., Ye, Z., & Wang, Y. (2020) · International Conference on Image, Video and Signal Processing (IVSP) · doi:10.1145/3388818.3389155
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance WeightsPreprint
Ericsson, L., Gouk, H., & Hospedales, T. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12360
Adversarial Knowledge Transfer from Unlabeled Data
Gupta, A., Panda, R., Paul, S., Zhang, J., & Roy–Chowdhury, A. K. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413688
Gray Scale Image Coloring Method Based on GAN
Guo, L. Y., Lu, L., Zeng, S., He, J., & Dai, G. (2020) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1145/3414274.3414485
DETECTION OF FAKE COLORIZED IMAGES USING DENSE CONVOLUTION NETWORK
Miranda, S. A., & Thulasimani, K. (2020)
A spatial hue similarity measure for assessment of colourisationPreprint
Mullery, S., & Whelan, P. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01700
Knowledge Extraction from Twitter Towards Infectious Diseases in Spanish
Arzube, O. O. A., García-Díaz, J. A., Luna-Aveiga, H., Medina-Moreira, J., & Valencia‐García, R. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-62015-8_4
Colour Palette as Support for CNN Colorization
Boutarfass, S., & Besserer, B. (2020) · International Conference on Multimedia Computing, Networking and Applications (MCNA) · doi:10.1109/mcna50957.2020.9264286
Recovery of a vandalized canvas painting our Lady of the Rosary from Vrlika (Croatia)
Šustić, S., Rezic, I., & Cvetković, M. (2020) · Ge-conservacion · doi:10.37558/gec.v18i1.828
More Is More -- Narrowing the Generalization Gap by Adding Classification HeadsPreprint
Cates, R., & Weinshall, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04924
Contrastive Spatial Reasoning on Multi-View Line Drawings.Preprint
Xiang, S., Yang, A., Xue, Y., Yang, Y. L., & Feng, C. (2021) · arXiv (Cornell University)
Cascading Modular Network (CAM-Net) for Multimodal Image SynthesisPreprint
Peng, S., Moazeni, A., & Li, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09015
TUCaN: Progressively Teaching Colourisation to CapsulesPreprint
Pucci, R., & Martinel, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15176
Leveraging Hidden Structure in Self-Supervised LearningPreprint
Sansone, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.16060
Colour for the Advancement of Deep Learning in Computer Vision
Akanuma, A. (2020) · Goldsmiths (University of London) · doi:10.25602/gold.00030181
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover MappingPreprint
Ghosh, R., Jia, X., Lin, C., Jin, Z., & Kumar, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07323 · Cited by 1
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation LearningPreprint
Al-Tahan, H., & Mohsenzadeh, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07082
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised LearningPreprint
Gidaris, S., Bursuc, A., Puy, G., Nikos Komodakis, A., Cord, M., & Pérez, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11552
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection TaskPreprint
Chen, J., Zhu, C., & Dai, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13435
EXPERIENCING STRESS DURING A CORONAVIRUS COVID-19 PANDEMIC: THE PERSPECTIVE OF AN AMBULANCE WORKER
Gutienė, V., & GLOBYTĖ, M. (2021) · HUMANITIES STUDIES · doi:10.26661/hst-2021-8-85-12
A Review and Analysis of The Existing Literature on Monochromatic Photography Colorization Using Deep Learning
Adascalitei, A. M. (2021) · Studia Universitatis Babeș-Bolyai Informatica · doi:10.24193/subbi.2021.2.03
Temporally-Embedded Self-Supervised Video Representation Learning
Wang, F., & Liu, H. (2021) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus52573.2021.9641272
ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization
Obuchowski, A., Klaudel, B., Karski, R., Rydziński, B., Glembin, M., Syty, P., & Jasik, P. (2022) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v35i.130689
Self-Supervised Continuous Meta-Learning for Few-shot Image Classification
-, S. H., & Li, Y. (2021) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait53529.2021.9731334
Review for "Deep learning as a tool for ecology and evolution"
(2022) · Publons reviews and discussion · doi:10.1111/2041-210x.13901/v1/review1
Pre-training Protein Models with Molecular Dynamics Simulations for Drug BindingPreprint
Wu, F., Zhang, Q., Radev, D., Wang, Y., Jin, X., Jiang, Y., Li, S. Z., & Niu, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-1566483/v1 · Cited by 2
Review for "Deep learning as a tool for ecology and evolution"
(2022) · Publons reviews and discussion · doi:10.1111/2041-210x.13901/v1/review4
Review for "Deep learning as a tool for ecology and evolution"
(2022) · Publons reviews and discussion · doi:10.1111/2041-210x.13901/v1/review2
Review for "Deep learning as a tool for ecology and evolution"
(2022) · Publons reviews and discussion · doi:10.1111/2041-210x.13901/v1/review3
Decision letter for "Deep learning as a tool for ecology and evolution"
(2022) · Publons reviews and discussion · doi:10.1111/2041-210x.13901/v2/decision1
An NLP approach to Image Analysis
Martinez, G. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kfbi5107
High-Fidelity Face Swapping with Style Blending
Yang, X., Guo, Z., Xue, M., & Shi, Z. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kupa8487 · Cited by 1
Pre- and Post-Operative Analysis of Planar Radiographs in Total Hip Replacement
Denton, O., Madden-McKee, C., Hill, J., Beverland, D., Dunne, N., & Lennon, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/exjl3727
A Data Augmentation and Pre-processing Technique for Sign Language Fingerspelling Recognition
Fowley, F., Rushe, E., & Ventresque, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/xbav3102
Beyond Social Distancing: Application of real-world coordinates in a multi-camera system with privacy protection
Ryan, F., Hu, F., Dietlmeier, J., O'Connor, N. E., & McGuinness, K. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/rtns4233
Acoustic Source Localization Using Straight Line Approximations
Bagchi, S., & de Fréin, R. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/ljrb7078
Detection and Isolation of 3D Objects in Unstructured Environments
Couto, D. D., Butterfield, J., Murphy, A., Rafferty, K., & Coleman, J. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/afmz9460
Deep learning for an improved diagnostic pathway of prostate cancer in a small multi-parametric magnetic resonance data regime
Fernández-Quilez, Á. (2022) · doi:10.31265/usps.197
Human face image colorization with dual-scale attention U-Net
Chen, L., Wang, B., & Lu, Z. (2022) · International Conference on Mechatronics and Computer Technology Engineering (MCTE) · doi:10.1117/12.2662622
Architecture Colorization via Self-supervised Learning and Instance Segmentation
Liu, S., Chen, H., & Li, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18907-4_1
SSL-DC: Improving Transductive Few-Shot Learning via Self-Supervised Learning and Distribution Calibration
Yang, H., Wang, D., Zhao, Z., & Wang, X. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956433
Equivariance Score Based on Canonical Correlation Analysis
Ohta, N., & Nobuhara, H. (2022) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis55246.2022.10001954
A Deep Multi-Layer Perceptron Model for Automatic Colourisation of Digital Grayscale Images
Shokunbi, O. M., Akinyemi, J. D., & Onifade, O. F. W. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-22025-8_14
Analysis of Different Losses for Deep Learning Image Colorization
Ballester, C., Carrillo, H., Clément, M., & Vitoria, P. (2023) · Springer eBooks · doi:10.1007/978-3-030-98661-2_127
Colorization
Liu, S. (2023) · Synthesis lectures on visual computing · doi:10.1007/978-3-031-26030-8_4
COLORIZING BLACK AND WHITE IMAGES USING CONDITIONAL GAN
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets35935
Ensemble deep learning fusion for detection of colorization based image forgeries
Shashikala, S., & K, R. G. (2023) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon57975.2023.10101337
Applying Depthwise Separated Neural Network with Color Space Adjustment to Auto-colorization of Thermal Infrared Images
Yeh, M.-T., Lo, W.-Y., Chung, Y., & Lu, P.-S. (2023) · Sensors and Materials · doi:10.18494/sam4296
An unsupervised transfer method for composited image to real image under dark light conditions
Wang, Z., Chen, W., Fang, Z., Zhang, H., Xie, L., Ye, Y., & Yin, E. (2023) · International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) · doi:10.1117/12.3004756
Evaluating self-supervised pre-training in out-of-distribution and low-data scenarios
Chaves, L. G. (2022) · doi:10.47749/t/unicamp.2022.1242931
Pixel representations, sampling, and label correction for semantic part detection
Huang, J.-C., Lin, Y.-L., & Fang, W.-C. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01493-0
Tiny Entropy Based Image Compression
Depoian, A. C., Adams, E. R., Bailey, C. P., & Guturu, P. (2023) · IEEE MetroCon · doi:10.1109/metrocon59008.2023.10340524
HiStyle: Reinventing Historic Portrait for 3D Colorization and Zero-Shot Stylization
Chen, Z., Yang, R., & Zhu Li, B. (2023) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp59012.2023.10337663
A Cross-domain Color Mapping from Exemplar Anime Image Colorization Networks
Peng, J., Chen, G., Saruta, K., & Terata, Y. (2023) · The Journal of the Society for Art and Science · doi:10.3756/artsci.22.14_1
SPC-Net: Structure-Aware Pixel-Level Contrastive Learning Network for OCTA A/V Segmentation and Differentiation
Liu, H., Hao, H., Ma, Y., Guo, L., Zhang, J., & Zhao, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-50069-5_20
McmIQA: Multi-Module Collaborative Model for No-Reference Image Quality Assessment
Miao, H., & Sang, Q. (2024) · Mathematics · doi:10.3390/math12081185 · Cited by 1
Realization of supermarket product image recognition based on automatic machine learning
Chen, Z. (2023) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc59590.2023.10507698
Background-focused contrastive learning for unpaired image-to-image translation
Shao, M.-W., Han, M., Meng, L., & Liu, F. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.4.043023 · Cited by 1
Unsupervised Feature Learning for Video Understanding
Wu, Z., & Jiang, Y. (2024) · Wireless networks · doi:10.1007/978-3-031-57679-9_6
Must Unsupervised Continual Learning Relies on Previous Information?
Cheng, H., Wen, H., Qiu, H., Wang, L., Zhang, M., & Li, H. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00561
Colorizing Images with Split-Brain Autoencoders and Convolutional Neural Networks
Krishna, B. B. S. M., Gupta, A., Reddy, K. J., & Vidhyalakshmi, M. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-68905-5_39
Periodic Iterative Segmentation-Colorization Training: Line Drawing Colorization Using Text Tag with CBAMCat
Luo, R., Yang, P., Zhou, N., & Liang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8502-5_16
Making the Primary Task Primary: Boosting Few-Shot Classification by Gradient-Biased Multi-task Learning
Wu, Y., Shi, B., Huo, J., Li, W., Gao, Y., Liu, H., Wang, Y., & Yu, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8487-5_24
Self-supervised Learning to Improve Froth Images Segmentation
Rumiantceva, M., Крюков, А. В., Prokopov, E., & Efimova, V. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5441-0_40

Authors: Larsson, G., Maire, M., & Shakhnarovich, G.
Affiliations: University of Chicago (US)
Toyota Technological Institute at Chicago (US)
Corresponding author: Larsson, Gustav
ORCID: Larsson, Gustav
Maire, Michael
Shakhnarovich, Gregory
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2016
Pages: 577-593
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-46493-0_35
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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

Learning Representations for Automatic Colorization

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks