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

Automatic Colorization of Images from Chinese Black and White Films Based on CNN

Chen, Y., Luo, Y., Ding, Y., & Yu, B. (2018) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2018.8455654 · Cited by 27

Broad Colorization

Jin, Y., Sheng, B., Li, P., & Chen, C. L. P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3004634 · Cited by 27

An improved pix2pix model based on Gabor filter for robust color image rendering

Li, H.-A., Zhang, M., Yu, Z., Li, Z., & Li, N. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022004 · Cited by 27

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

Fu, Y., & Feng, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892218 · Cited by 34

Self-Supervised Representation Learning for CAD

Jones, B. T., Hu, M., Kodnongbua, M., Kim, V. G., & Schulz, A. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.02043 · Cited by 41

Self-Supervised Learning with Kernel Dependence MaximizationPreprint

Li, Y., Pogodin, R., Sutherland, D. J., & Gretton, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08320 · Cited by 34

On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs

Banerjee, S., Scheirer, W. J., Bowyer, K. W., & Flynn, P. J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093568 · Cited by 26

Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images

Fan, L., Sowmya, A., Meijering, E. H. W., & Song, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_57 · Cited by 26

Learning Based Image Transformation Using Convolutional Neural Networks

Hou, X., Gong, Y., Liu, B., Sun, K., Liu, J., Xu, B., Duan, J., & Qiu, G. (2018) · IEEE Access · doi:10.1109/access.2018.2868733 · Cited by 26

SDCluster: A clustering based self-supervised pre-training method for semantic segmentation of remote sensing images

Xu, H., Zhang, C., Yue, P., & Wang, K. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.02.021 · Cited by 27

Modularized Textual Grounding for Counterfactual Resilience

Fang, Z., Kong, S., Fowlkes, C. C., & Yang, Y. (2019) · 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.2019.00654 · Cited by 26

A study of the generalizability of self-supervised representations

Tendle, A., & Hasan, M. R. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100124 · Cited by 28

Learning to Align Sequential Actions in the Wild

Liu, W., Tekin, B., Coskun, H., Vineet, V., Fua, P., & Pollefeys, M. (2022) · 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/cvpr52688.2022.00222 · Cited by 25

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

Park, J., Kwak, I.-Y., & Lim, C. (2021) · Electronics · doi:10.3390/electronics10161996 · Cited by 24

MRI Image-to-Image Translation for Cross-Modality Image Registration and Segmentation.

Yang, Q., Li, N., Zhao, Z., Fan, X., Chang, E., & Xu, Y. L. (2018) · Cited by 24

Joint CNN and Variational Model for Fully-Automatic Image Colorization

Mouzon, T., Pierre, F., & Berger, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22368-7_42 · Cited by 24

Video Forensics: Identifying Colorized Images Using Deep Learning

Ulloa, C., Ballesteros, D. M., & Renza, D. (2021) · Applied Sciences · doi:10.3390/app11020476 · Cited by 24

TATL: Task agnostic transfer learning for skin attributes detection

Nguyen, M.-D., Nguyen, T., Vu, H., Pham, Q., Nguyen, M.-D., Nguyen, B. T., & Sonntag, D. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102359 · Cited by 24

Unsupervised image-to-image translation via long-short cycle-consistent adversarial networks

Wang, G., Shi, H., Chen, Y., & Wu, B. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04389-0 · Cited by 24

Automatic Color Correction for Multisource Remote Sensing Images with Wasserstein CNN

Guo, J., Pan, Z., Lei, B., & Ding, C. (2017) · Remote Sensing · doi:10.3390/rs9050483 · Cited by 24

Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network

Nam, S., & Kim, S. J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.190 · Cited by 24

Automatic gray images colorization based on lab color space

Abbadi, N. K. E., & Saleem, E. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v18.i3.pp1501-1509 · Cited by 24

Exploring Simple and Transferable Recognition-Aware Image Processing

Liu, Z., Wang, H.-J., Zhou, T., Shen, Z., Kang, B., Shelhamer, E., & Darrell, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183243 · Cited by 27

Dynamic Spatio-Temporal Graph Reasoning for VideoQA With Self-Supervised Event Recognition

Nie, J., Wang, X. Y., Hou, R., Li, G., Chen, H., & Zhu, W. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3411448 · Cited by 27

Hierarchical Self-supervised Learning for Medical Image Segmentation Based on Multi-domain Data Aggregation

Zheng, H., Han, J., Wang, H., Yang, L., Zhao, Z., Wang, C., & Chen, D. Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_59 · Cited by 23

Focusing on Persons

Jin, X., Li, Z., Liu, K., Zou, D., Li, X., Zhu, X., Zhou, Z., Sun, Q., & Liu, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3481544 · Cited by 24

SSL++: Improving Self-Supervised Learning by Mitigating the Proxy Task-Specificity Problem

Song, C., Xue, J., Chang, J., Zhang, J., Yang, J., & Tian, Q. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3135470 · Cited by 23

Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

Lerma, M., & Lucas, M. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/awjv6348 · Cited by 26

Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond

Fei, Z., Fan, M., Zhu, L., Huang, J., Wei, X., & Wei, X. K. (2023) · 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/cvpr52729.2023.02342 · Cited by 24

Asymmetric Cyclegan for Unpaired NIR-to-RGB Face Image Translation

Dou, H., Chen, C., Hu, X., & Peng, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682600 · Cited by 23

Learning Pose Specific Representations by Predicting Different Views

Poier, G., Schinagl, D., & Bischof, H. (2018) · 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.2018.00014 · Cited by 22

Self-supervised learning for medieval handwriting identification: A case study from the Vatican Apostolic Library

Lastilla, L., Ammirati, S., Firmani, D., Komodakis, N., Merialdo, P., & Scardapane, S. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102875 · Cited by 24

Autoregressive Predictive Coding: A Comprehensive Study

Yang, G.-P., Yeh, S.-L., Chung, Y.-A., Glass, J. R., & Tang, H. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3203608 · Cited by 24

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

Yuan, J. S., Hou, F., Du, Y., Shi, Z., Geng, X., Jianping Fan, B., & Rui, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548121 · Cited by 22

SVCNet: Scribble-Based Video Colorization Network With Temporal Aggregation

Zhao, Y., Po, L.-M., Liu, K., Wang, X., Yu, W.-Y., Xian, P., Zhang, Y., & Liu, M. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3298537 · Cited by 22

A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical microscopy images

Chen, W., Li, C., Chen, D., & Luo, X. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.001 · Cited by 22

Inverse Biometrics: Generating Vascular Images From Binary Templates

Kauba, C., Kirchgasser, S., Mirjalili, V., Uhl, A., & Ross, A. A. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3073666 · Cited by 21

Retrieval of Hyperspectral Surface Reflectance Based on Machine Learning

Zhu, S., Lei, B., & Wu, Y. (2018) · Remote Sensing · doi:10.3390/rs10020323 · Cited by 21

Self-supervised deep metric learning for ancient papyrus fragments retrieval

Pirrone, A., Beurton‐Aimar, M., & Journet, N. (2021) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-021-00369-1 · Cited by 24

Ranking CGANs: Subjective Control over Semantic Image AttributesPreprint

Saquil, Y., Kim, K. I., & Hall, P. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04082 · Cited by 23

Image Colorization with Convolutional Neural Networks

An, J., Gagnon, K. K., Shi, Q., Xie, H., & Cao, R. (2019) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei48845.2019.8965756 · Cited by 20

An Extensive Study on Deep Learning: Techniques, Applications

Mittal, R., Arora, S., Bansal, V., & Bhatia, M. P. S. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09542-5 · Cited by 21

Adversarial Edge-Aware Image Colorization With Semantic Segmentation

Kong, G., Tian, H., Duan, X., & Long, H. (2021) · IEEE Access · doi:10.1109/access.2021.3056144 · Cited by 20

Dual class representation learning for few-shot image classification

Singh, P. P., & Mazumder, P. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107840 · Cited by 20

Anomaly Detection Requires Better Representations

Reiss, T., Cohen, N., Horwitz, E., Abutbul, R., & Hoshen, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25069-9_4 · Cited by 25

Prototype-Augmented Self-Supervised Generative Network for Generalized Zero-Shot Learning

Wu, J., Zhang, T., Zha, Z.-J., Luo, J.-B., Zhang, Y., & Wu, F. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3351439 · Cited by 19

Evaluation of the aeroengine performance reliability based on generative adversarial networks and Weibull distribution

Fu, Q., Wang, H., & Yan, X. (2019) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410019856187 · Cited by 20

Bridging the gap between Natural and Medical Images through Deep Colorization

Morra, L., Piano, L., Lamberti, F., & Tommasi, T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412444 · Cited by 22

An Overview of Grayscale Image Colorization Methods

Žeger, I., & Grgić, S. (2020) · International Symposium ELMAR (ELMAR) · doi:10.1109/elmar49956.2020.9219019 · Cited by 20

Modeling Artistic Workflows for Image Generation and Editing

Tseng, H.-Y., Fisher, M., Lu, J., Li, Y., Kim, V. G., & Yang, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_10 · Cited by 20

Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Zhuo, L., Tan, S., Zeng, J., & Lit, B. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659761 · Cited by 20

Universal 12-lead ECG representation for signal denoising and cardiovascular disease detection by fusing generative and contrastive learning

Shi, J., Liu, W., Zhang, H., Li, Z., Chang, S., Wang, H., He, J. W., & Huang, Q. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106253 · Cited by 19

Benchmarking Omni-Vision Representation Through the Lens of Visual Realms

Zhang, Y., Yin, Z., Shao, J., & Liu, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20071-7_35 · Cited by 21

DDGAN: Dense Residual Module and Dual-stream Attention-Guided Generative Adversarial Network for colorizing near-infrared images

Chen, Y., Zhan, W., Jiang, Y., Zhu, D., Guo, R., & Xu, X. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104822 · Cited by 19

Visual–language foundation models in medicine

Liu, C., Jin, Y., Guan, Z., Li, T., Qin, Y., Qian, B., Jiang, Z., Wu, Y.-L., Wang, X., Zheng, Y. F., et al. (2024) · The Visual Computer · doi:10.1007/s00371-024-03579-w · Cited by 25

Contrastive self-supervised learning for neurodegenerative disorder classification

Gryshchuk, V., Singh, D. S., Teipel, S. J., Dyrba, M., & ADNI, A. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1527582 · Cited by 21

CDGAN: Cyclic Discriminative Generative Adversarial Networks for image-to-image transformation

Babu, K. K., & Dubey, S. R. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103382 · Cited by 19

Grayscale images colorization with convolutional neural networks

An, J., Gagnon, K. K., & Shi, Q. (2020) · Soft Computing · doi:10.1007/s00500-020-04711-3 · Cited by 19

Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation

Li, K., Peng, S., Zhang, T., & Malik, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01325-y · Cited by 22

Self-supervised representation learning by predicting visual permutations

Zhao, Q., & Dong, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106534 · Cited by 19

Unsupervised Image Classification for Deep Representation Learning

Chen, W., Pu, S., Xie, D., Yang, S., Guo, Y., & Lin, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_30 · Cited by 19

Self-Supervised Motion Perception for Spatiotemporal Representation Learning

Liu, C., Yao, Y., Luo, D., Zhou, Y., & Ye, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160860 · Cited by 19

Self-Supervised Learning for Text Recognition: A Critical Survey

Penarrubia, C., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02487-3 · Cited by 19

Line Art Colorization Based on Explicit Region Segmentation

Cao, R., Mo, H., & Gao, C. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14396 · Cited by 18

Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression

Seo, D. K., Kim, Y., Eo, Y. D., & Park, W. Y. (2018) · Applied Sciences · doi:10.3390/app8081269 · Cited by 18

Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture

Johari, M. M., & Behroozi, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.042 · Cited by 18

Double-Channel Guided Generative Adversarial Network for Image Colorization

Du, K., Liu, C., Cao, L., Guo, Y., Zhang, F., & Wang, T. (2021) · IEEE Access · doi:10.1109/access.2021.3055575 · Cited by 18

Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains

Yang, H., Tang, S., Chen, M., Wang, Y., Zhu, F., Bai, L., Zhao, R., & Ouyang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_9 · Cited by 20

Color2Embed: Fast Exemplar-Based Image Colorization using Color EmbeddingsPreprint

Zhao, H., Wu, W., Liu, Y., & He, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08017 · Cited by 21

Understanding Negative Samples in Instance Discriminative Self-supervised Representation LearningPreprint

Nozawa, K., & Sato, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06866 · Cited by 21

A Flexible Selection Scheme for Minimum-Effort Transfer Learning

Royer, A., & Lampert, C. H. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093635 · Cited by 17

Progressive Cluster Purification for Unsupervised Feature Learning

Zhang, Y., Liu, C., Zhou, Y., Wang, W., Wang, W., & Ye, Q. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412301 · Cited by 17

GenN2N: Generative NeRF2NeRF Translation

Liu, X., Han, X., Luo, K., Tan, P., & Yi, L. (2024) · 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/cvpr52733.2024.00488 · Cited by 19

Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks

Bhunia, A. K., Bhunia, A. K., Banerjee, P., Konwer, A., Bhowmick, A., Roy, P. P., & Pal, U. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545184 · Cited by 17

Deep Inverse Halftoning via Progressively Residual Learning

Xia, M., & Wong, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_33 · Cited by 17

Learn to Recover Visible Color for Video Surveillance in a Day

Wu, G., Zheng, Y., Guo, Z., Cai, Z., Shi, X., Ding, X., Huang, Y., Guo, Y., & Shibasaki, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_29 · Cited by 17

WGGAN: A wavelet-guided generative adversarial network for thermal image translation

Zhang, R., Bin, J., Liu, Z., & Blasch, E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823519-5.00015-4 · Cited by 17

A Dual-Generator Translation Network Fusing Texture and Structure Features for SAR and Optical Image Matching

Nie, H., Fu, Z., Tang, B., Li, Z., Chen, S., & Wang, L. (2022) · Remote Sensing · doi:10.3390/rs14122946 · Cited by 17

Multi‐style Chinese art painting generation of flowers

Fu, F., Lv, J., Tang, C., & Li, M. (2020) · IET Image Processing · doi:10.1049/ipr2.12059 · Cited by 16

Geographical Supervision Correction for Remote Sensing Representation Learning

Li, W., Chen, K., & Shi, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3202499 · Cited by 19

Self-Supervised Learning With Prediction of Image Scale and Spectral Order for Hyperspectral Image Classification

Yang, X., Cao, W., Lu, Y., & Zhou, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3225663 · Cited by 16

A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Gain, M., & Debnath, R. (2023) · IEEE Access · doi:10.1109/access.2023.3263948 · Cited by 16

Contrastive Learning Based on Multiscale Hard Features for Remote-Sensing Image Scene Classification

Li, Z., Hou, B., Guo, X., Ma, S., Cui, Y., Wang, S., & Jiao, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3291878 · Cited by 17

Hierarchical Clustering and Refinement for Generalized Multi-Camera Person Tracking

Li, Z., Wang, R., Li, H., Wei, B., Shi, Y., Ling, H., Chen, J., Liu, B., Li, Z., & Zheng, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00584 · Cited by 16

Exemplar-Based Continual Learning via Contrastive Learning

Chen, S., Zhang, M., Zhang, J., & Huang, K. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3355879 · Cited by 17

Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation

Yuan, J. S., Hou, F., Ying, Y., Zhang, Y. S., Shi, Z., Geng, X., Jianping Fan, B., He, Z., & Rui, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3361729 · Cited by 16

A feature refinement and adaptive generative adversarial network for thermal infrared image colorization

Chen, Y., Zhan, W., Jiang, Y., Zhu, D., Xu, X., Hao, Z., Li, J., & Guo, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106184 · Cited by 16

Weakly Supervised Semantic Segmentation of Remote Sensing Images Using Siamese Affinity Network

Chen, Z., Lian, Y., Bai, J., Zhang, J., Xiao, Z. Q., & Hou, B. (2025) · Remote Sensing · doi:10.3390/rs17050808 · Cited by 16

Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task

Dutta, S., & Gros, E. (2018) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2293395 · Cited by 16

Tile Art Image Generation Using Conditional Generative Adversarial Networks

Matsumura, N., Tokura, H., Kuroda, Y., Ito, Y., & Nakano, K. (2018) · International Symposium on Computing and Networking Workshops (CANDARW) · doi:10.1109/candarw.2018.00047 · Cited by 16

Emerging applications of reversible data hiding

Hou, D., Zhang, W., Liu, J., Zhou, S., Chen, D., & Yu, N. (2019) · International Conference on Image and Graphics Processing (ICIGP) · doi:10.1145/3313950.3313952 · Cited by 16

NTIRE 2019 Challenge on Image Colorization: Report

Gu, S., Timofte, R., Zhang, R., Suin, M., Purohit, K., Rajagopalan, A. N., Athi Narayanan, S., Pinjari, J. B., Xiong, Z., Shi, Z., et al. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00276 · Cited by 16

JPEG Robust Invertible Grayscale

Liu, K., Chen, D., Liao, J., Zhang, W., Zhou, H., Zhang, J., Zhou, W., & Yu, N. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3088531 · Cited by 16

Implicit and Explicit Attention for Zero-Shot Learning

Alamri, F., & Dutta, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92659-5_30 · Cited by 16

Task-Level Self-Supervision for Cross-Domain Few-Shot Learning

Yuan, W., Zhang, Z., Wang, C., Song, H., Xie, Y., & Ma, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20230 · Cited by 16

Bootstrapped Representation Learning for Skeleton-Based Action Recognition

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