Learning Representations for Automatic Colorization

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

The Power of Deep Learning Models: Applications

(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1468.0982s1119 · Cited by 2

Image Colorization Algorithm Based on Improved GAN

Wu, W., Li, Y., & Che, C. (2021) · International Conference on Computer, Blockchain and Financial Development (CBFD) · doi:10.1109/cbfd52659.2021.00016 · Cited by 2

Colorization of Grayscale Images Using Convolutional Neural Network and Siamese Network

Kumar, A., George, D. S., & Binu, L. S. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-9650-3_23 · Cited by 2

Towards Label-Free Few-Shot Learning: How Far Can We Go?

Bharti, A., Vineeth, N. B., & Jawahar, C. V. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-11346-8_23 · Cited by 2

Channel Difference Based Regeneration Architecture for Fake Colorized Image Detection

Phutke, S. S., & Murala, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-11349-9_7 · Cited by 2

Texture improvement for human shape estimation from a single image

Escribano, J. G., Rauno, S., Swaminathan, A., Smyth, D., & Smolić, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/soww6683 · Cited by 2

Dynamic Channel Selection in Self-Supervised Learning

Krishna, T., Rai, A., Djilali, Y. A. D., Smeaton, A. F., McGuinness, K., & O'Connor, N. E. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/lkli8696 · Cited by 2

Learned Variational Video Color Propagation

Hofinger, M., Kobler, E., Effland, A., & Pock, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_30 · Cited by 2

Generative Adversarial Network for Colorization of Mammograms

Khimani, M., Raj, S., Oza, U., & Kumar, P. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5037-7_2 · Cited by 2

VCL-PL:Semi-Supervised Learning from Noisy Web Data with Variational Contrastive Learning

Yavuz, M. C., & Yanikoglu, B. A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956152 · Cited by 2

Application of a Self-supervised Learning Technique for Monitoring Industrial Spaces

Magalhães, V., Costa, M. F. P., Ferreira, M. J., Pinto, T. D. O., & Figueiredo, V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37105-9_27 · Cited by 2

Detecting abnormal cell behaviors from dry mass time series

Bailly, R., Malfante, M., Allier, C. P., Paviolo, C., Ghenim, L., Padmanabhan, K., Bardin, S., & Mars, J. I. (2024) · Scientific Reports · doi:10.1038/s41598-024-57684-w · Cited by 3

DisRot: boosting the generalization capability of few-shot learning via knowledge distillation and self-supervised learning

Ma, C., Jia, J., Huang, J., Wu, L., & Wang, X. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01529-z · Cited by 2

Bi-Stream Feature Extraction and Multiscale Attention Generative Adversarial Network (BM-GAN): Colorization of GrayScale Images Based on Bi-Stream Feature Fusion and Multiscale Attention Generative Adversarial Network

Gao, X., Yin, L., Deng, Y., Wang, F., Qin, Y., & zhang, M. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554241297613 · Cited by 2

Local Masked Reconstruction for Efficient Self-Supervised Learning on High-Resolution Images

Chen, J., Khan, F. F., Hu, M., Sherif, A., Ge, Z., Li, B., & Elhoseiny, M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00781 · Cited by 3

UniEmoX: Cross-Modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception

Chen, C., Sun, X. N., & Liu, Z. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3587577 · Cited by 4

A Novel Approach for Colorization of a Grayscale Image using Soft Computing Techniques

Hasnat, A., Halder, S., Bhattacharjee, D., & Nasipuri, M. (2017) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2017100102 · Cited by 2

Deep Colorization by Variation

Tang, Z. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358085 · Cited by 2

Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks

Leordeanu, M. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-42128-1_7 · Cited by 2

Pro-CCaps: Progressively Teaching Colourisation to Capsules

Pucci, R., Micheloni, C., Foresti, G. L., & Martinel, N. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00311 · Cited by 2

Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

Wu, Y., Lv, C., Ding, B., Chen, L., Zhou, B., & Zhou, H. (2022) · Electronics · doi:10.3390/electronics11040639 · Cited by 2

Joint intensity–gradient guided generative modeling for colorization

Xiong, K., Hong, K., Li, J., Li, W., Liao, W., & Liu, Q. (2022) · The Visual Computer · doi:10.1007/s00371-022-02747-0 · Cited by 2

A Deep Convolutional Autoencoder Architecture for Automatic Image Colorization

Cevallos, S. B., Pérez, N., Riofrío, D., Benítez, D. S., Moyano, R. F., & Baldeon-Calisto, M. (2022) · IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) · doi:10.1109/colcaci56938.2022.9905295 · Cited by 2

Colorization of Grayscale Images: An Overview

Wyawahare, M. V., Kolhe, T., Joshi, A. V., Metkari, Y., & Patil, K. (2023) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-15175-0_12 · Cited by 2

Automatic video colorization based on contrastive learning and optical flow

Xiao, S., Wang, Y., & Wang, Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17883-5 · Cited by 2

ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation

Dhiman, A., Srinath, R., Sarkar, S., Boregowda, L. R., & Babu, R. V. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00239 · Cited by 2

Analysis of Unsupervised Training Approaches for LSTM-Based OCR

Jenckel, M., Bukhari, S. S., & Dengel, A. R. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00202 · Cited by 2

MLEU

Nguyen-Quynh, T.-T., Do, N.-T., & Kim, S.-H. (2020) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3380688.3380720 · Cited by 2

Fake colorized and morphed image detection using convolutional neural network

Pillai, N. (2020) · ACCENTS Transactions on Image Processing and Computer Vision · doi:10.19101/tipcv.2020.618011 · Cited by 2

Don’t miss the mismatch: investigating the objective function mismatch for unsupervised representation learning

Stuhr, B., & Brauer, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07031-9 · Cited by 2

Self-supervised adversarial learning for cross-modal retrieval

Wang, Y., He, S., Xu, X., Yang, Y., Li, J., & Shen, H. T. (2021) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3444685.3446269 · Cited by 2

Image Representation Learning by Transformation Regression

Guo, X., Liu, J., Zhou, S., Zhu, E., & Dong, S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412597 · Cited by 2

A Novel Multi-Task Self-Supervised Representation Learning Paradigm

Li, Y., Hu, J., Sun, J., Zhao, S., Zhang, Q., & Lin, Y. (2021) · IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) · doi:10.1109/aiid51893.2021.9456562 · Cited by 2

Prototype for Integration of colorization and super resolution model using machine learning

Lodh, A., Saxena, U., Khan, A., Atulkumar, K. A. P. H., & Sountharranjan, S. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544827 · Cited by 2

DETECTren: Vehicle Object Detection Using Self-Supervised Learning based on Light-Weight Network for Low-Power Devices

Fuhrmann, H., Boyko, A., Abdelpakey, M. H., & Shehata, M. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9594927 · Cited by 2

DeepDiffusion: Unsupervised Learning of Retrieval-Adapted Representations via Diffusion-Based Ranking on Latent Feature Manifold

Furuya, T., & Ohbuchi, R. (2022) · IEEE Access · doi:10.1109/access.2022.3218909 · Cited by 2

Self-Supervised Video Representation Learning with Motion-Contrastive Perception

Liu, J., Cheng, Y., Zhang, Y., Zhao, R.-W., & Feng, R. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859802 · Cited by 2

Relative Contrastive Loss for Unsupervised Representation Learning

Tang, S., Zhu, F., Bai, L., Zhao, R., & Ouyang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_1 · Cited by 2

Instance Separation Emerges from InpaintingPreprint

Wolf, S., Hamprecht, F. A., & Funke, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00891 · Cited by 2

Automatic Image Labelling at Pixel LevelPreprint

Zhang, X., Zhang, W., Peng, J., & Jianping Fan, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07415 · Cited by 2

Is It a Plausible Colour? UCapsNet for Image ColourisationPreprint

Pucci, R., Micheloni, C., Foresti, G. L., & Martinel, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02478 · Cited by 2

THAT: Two Head Adversarial Training for Improving Robustness at ScalePreprint

Wu, Z., Goldstein, T., Davis, L. S., & Lim, S.-N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13612 · Cited by 2

ViT-Inception-GAN for Image ColourisingPreprint

Bana, T., Loya, J., & Kulkarni, S. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06321 · Cited by 2

Inverse Problems in Asteroseismology

Bellinger, E. P. (2019) · doi:10.53846/goediss-6957 · Cited by 2

Spectral Super-Resolution for High Dynamic Range Images

Mikamoto, Y., Kaminaka, Y., Higaki, T., Raytchev, B., & Kaneda, K. (2023) · Journal of Imaging · doi:10.3390/jimaging9040083 · Cited by 1

Image Colorization

Gaikwad, S. S. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.61798 · Cited by 1

DBSF-Net: Infrared Image Colorization Based on the Generative Adversarial Model with Dual-Branch Feature Extraction and Spatial-Frequency-Domain Discrimination

Li, S., Ma, D., Ding, Y., Xian, Y., & Zhang, T. (2024) · Remote Sensing · doi:10.3390/rs16203766 · Cited by 1

SemanticColorizer: Improving color semantic rationality for vivid automatic image colorization

Chai, X., Cheng, Z., Zhang, H., Xie, R., & Song, L. (2025) · Displays · doi:10.1016/j.displa.2025.103172 · Cited by 1

Removal of spatial inconsistencies in automated image colorization using parameter-free clustering and convolutional neural networks

Singh, N., Gupta, G., Singh, A., Kishore, A., Kumar, K. S., & Bharti, D. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02047-5 · Cited by 1

CCJ-SLC: A Skin Lesion Image Classification Method Based on Contrastive Clustering and Jigsaw Puzzle

Zhang, Y., Xu, G., & Wu, C. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8558-6_3 · Cited by 1

Hierarchical Color Fusion Network (HCFN): Enhancing exemplar-based video colorization

Yin, W., Lu, P., Peng, X., Zhao, Z., & Yu, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128121 · Cited by 1

Technology, Science and Culture – A Global Vision, Volume V

Santos, S. S. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.115472 · Cited by 1

SAGE: Spatially Aware Guided Emotion color transfer with category-adaptive transformation

Choi, T., & Park, J. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.106058 · Cited by 1

Exemplar-based image colorization with awareness of object co-saliency

Chiu, Y.-H., Chang, K.-H., & Lin, I. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21143-7 · Cited by 1

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

Yan, D., Bai, L., Zhang, Y., & Han, J. (2017) · Optical Review · doi:10.1007/s10043-017-0398-8 · Cited by 1

Matching Low-Quality Photo to DSLR-Quality with Deep Convolutional Networks

Xia, W., Yang, C. G., Yang, Y., & Sun, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94361-9_4 · Cited by 1

Depth-Aware Image Colorization Network

Chu, W.-T., & Hsu, Y.-T. (2018) · doi:10.1145/3267799.3267800 · Cited by 1

Colourization of Low-Light-Level Images Based on Rule Mining

Bai, L., Han, J., & Yue, J. (2019) · Springer eBooks · doi:10.1007/978-981-13-1669-2_8 · Cited by 1

Color Restoration Survey and an Overdetermined System for Color Retrieval from Faded Images

Haslam, D., Ibrahim, S., & Mesalami, A. E. (2020) · Springer eBooks · doi:10.1007/978-3-030-39847-7_24 · Cited by 1

Auto Neural Architecture Generator for UAV-Based Geospatial Surveillance for Aerial Crisis Mapping in Dark

Rai, A., & Kannan, R. J. (2020) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-020-01236-y · Cited by 1

Vein Pattern Visualisation using Conditional Generative Adversarial Networks

Keivanmarz, A., Sharifzadeh, H., & Fleming, R. I. (2020) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · Cited by 1

Line Drawing Extraction from Cartoons Using a Conditional Generative Adversarial Network

Yu, K., Noh, J.-H., & Yang, H.-D. (2021) · Applied Sciences · doi:10.3390/app11167536 · Cited by 1

Video representation learning by identifying spatio-temporal transformations

Geng, S., Zhao, S., & Hu, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02790-9 · Cited by 1

Colorizing images with Conditional Adversarial Networks and Transfer Learning

Treneska, S., & Jovanov, M. (2021) · IEEE International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy52375.2021.9582537 · Cited by 1

LineGAN: An image colourisation method combined with a line art network

Lv, D., Pu, Y., & Nie, R. (2022) · IET Computer Vision · doi:10.1049/cvi2.12096 · Cited by 1

Stacked Multi-Scale Attention Network for Image Colorization

Jiang, B., Xu, F., Xia, J., Yang, C., Huang, W., & Huang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746133 · Cited by 1

Improving colorization through denoiser with MLP

Park, C.-R., Kim, J., & Cho, S.-J. (2022) · Journal of Advanced Marine Engineering and Technology · doi:10.5916/jamet.2022.46.2.85 · Cited by 1

Auto-Image Colorization Of Grayscale Images Using Machine Learning Techniques

Puspaningrum, E. Y., Saputra, W. S. J., & Setiawan, A. (2021) · IEEE Information Technology International Seminar (ITIS) · doi:10.1109/itis53497.2021.9791518 · Cited by 1

Semi-Supervised Cloud Detection for Remote Sensing Imagery via Self-Training

ZHAO, C. F., Zhang, X., Luo, H., Zhong, S., Tang, L., Peng, J., & Jianping Fan, B. (2022) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica54878.2022.9844616 · Cited by 1

A Lightweight Image Colorization Model Based on U-Net Architecture

Thanh, P. V., & Hung, P. D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2894-9_7 · Cited by 1

Classification of electromagnetic interference induced image noise in an analog video link

Purcell, A. W., & Eising, C. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/zanb2627 · Cited by 1

Reproducibility Companion Paper: Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies

Jin, X., Liu, K., Zou, D., Li, Z., Huang, H., & Thambawita, V. L. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548526 · Cited by 1

Ethnic Costume Grayscale Image Coloring Method with Improved Pix2Pix

Tang, X., & Wen, B. (2022) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess54813.2022.9930271 · Cited by 1

Attention-Based Unsupervised Sketch Colorization of Anime Avatar

Zhang, S., & Zhou, J. (2022) · International Conference on Control and Intelligent Robotics (ICCIR) · doi:10.1145/3548608.3559316 · Cited by 1

Enhancement and colorized a noisy image using MATLAB and AI libraries

Salman, D. S., & Naser, H. A. (2023) · AIP conference proceedings · doi:10.1063/5.0119540 · Cited by 1

Detection of Fake Colorized Images based on Deep Learning

Salman, K. A., Shaker, K., & Al-Janabi, S. T. F. (2023) · International Journal of Image and Graphics · doi:10.1142/s0219467825500020 · Cited by 1

Tinction of Black and White Images using a Novel Deep Learning Approach

Kiran, K. S., Jha, A. K., & Jeyavathana, R. B. (2023) · International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) · doi:10.1109/accai58221.2023.10199498 · Cited by 1

Unsupervised Fashion Style Learning by Solving Fashion Jigsaw Puzzles

Chen, J., Yuan, H., Fang, F., Peng, T., & Hu, X. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00317 · Cited by 1

COLORFLOW: A Conditional Normalizing Flow for Image Colorization

Yin, W., Lu, P., & Peng, X. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448464 · Cited by 2

Reference-based Video Colorization with AB Chrominance Point and Temporal Propagation

Yang, Y., Jin, J., Huang, Y., Guo, K., & Xu, X. (2024) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3651671.3651767 · Cited by 1

Self-supervised few-shot medical image segmentation with spatial transformations

Titoriya, A. K., Singh, M. P., & Singh, A. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10184-4 · Cited by 1

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?

Özbulak, U., Anzaku, E. T., Kang, S., Neve, W. D., & Vankerschaver, J. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650017 · Cited by 2

Masked Autoencoders are Secretly Efficient Learners

Wei, Z., Wei, C., Mei, J., Bai, Y., Wang, Z., Li, X., Zhu, H., Wang, H., Yuille, A., Zhou, Y., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00797 · Cited by 1

Automatic Colorization of Greyscale Images Using Deep Learning Fusion Technique and Accuracy Level Analysis

Akarawita, I., & Abeykoon, A. M. H. S. (2024) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm62033.2024.10715939 · Cited by 1

Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation

Seong, H. S., Moon, W., Lee, S., & Heo, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72967-6_26 · Cited by 1

High Autoencoder Based System for Seamless Transformation of Grayscale Portraits into Vibrant Color Representations

Sarna, A., Gadwal, A., Choundawat, J. S., Kate, V., & Sharan, M. (2024) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset62108.2024.10743890 · Cited by 1

Negative Sampling for Triplet-Based Loss: Improving Representation in Self-supervised Representation Learning

Goyo, M. A., & Hidalgo, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76607-7_10 · Cited by 1

Interactive Video Colorization With Memory Units and Feature Correction

Zhang, J., Xiao, Y., Qiao, J., Sun, Q., Zheng, Y., & Leung, C.-S. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3514111 · Cited by 1

SCD: Statistical Color Distribution-Based Objective Image Colorization Quality Assessment

Lyu, H., Elangovan, H., Rosin, P. L., & Lai, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81806-6_4 · Cited by 1

KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder

Bora, M., Atreya, S., Mukherjee, A., & Das, A. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00731 · Cited by 1

Deep Learning For Image Colorization: An Encoder-Decoder Approach

Aalam, M. Z., Faiz, A., Tandi, I., Bhagat, A., & Vishwakarma, M. P. (2025) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.6.0425.14158 · Cited by 1

Self-supervised GAN for Image Generation by Correlating Image Channels

Qian, S., Cao, W., Li, R., Wu, S., & Wong, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00767-6_8 · Cited by 1

Method for Coloring Night-vision Imagery Based on Multispectral Semantic Segmentation

Zhang, W., Gu, X., & Gu, X. (2018) · Australian & New Zealand Control Conference (ANZCC) · doi:10.1109/anzcc.2018.8606609 · Cited by 1

Wiener Loss

Li, J., Liu, Y., Xiao, H., Tan, H., & Zhang, M. (2018) · International Conference on Artificial Intelligence and Virtual Reality (AIVR) · doi:10.1145/3293663.3293674 · Cited by 1

InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks

Wei, Q., Fan, K., Wang, W., Zheng, T., Amit, C., Heller, K., Chen, C., & Ren, K. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00230 · Cited by 1

Pseudo-Cyclic Network for Unsupervised Colorization with Handcrafted Translation and Output Spatial Pyramids

Ratajczak, R., Crispim-Junior, C. F., Fervers, B., Faure, É., & Tougne, L. (2019) · Workshop on Structuring and Understanding of Multimedia heritAge Contents (SUMAC) · doi:10.1145/3347317.3357243 · Cited by 1

Automatic Natural Image Colorization

Tran, T.-B., & Tran, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41964-6_53 · Cited by 1

Colorization of Characters Based on the Generative Adversarial Network

Liu, C., Cao, L., & Du, K. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-48513-9_51 · Cited by 1

Equivariance and Invariance for Robust Unsupervised and Semi-Supervised Learning

Zhang, L. (2020) · Journal of International Crisis and Risk Communication Research · Cited by 1

REVEALING LONG-TERM PHYSIOLOGICAL TRAJECTORIES OF GRASSLANDS FROM LEGACY B&W AERIAL PHOTOGRAPHS

Poterek, Q., Herrault, P., Forestier, G., & Schwartz, D. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-3-2020-549-2020 · Cited by 1

Image Colorization Using a Deep Transfer Learning

Kiani, L., Saeed, M., & Nezamabadi–pour, H. (2020) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis49607.2020.9238737 · Cited by 9

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

Semantic‐aware automatic image colorization via unpaired cycle‐consistent self‐supervised network

Xiao, Y., Jiang, A., Liu, C., & Wang, M. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22667 · Cited by 14

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

Automatic Natural Image Colorization

Tran, T.-B., & Tran, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41964-6_53 · Cited by 1