How Useful Is Photo-Realistic Rendering for Visual Learning?
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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 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
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 · see citing works
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228 · see citing works
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321 · see citing works
EPnP: An Accurate O(n) Solution to the PnP Problem
Lepetit, V., Moreno-Noguer, F., & Fua, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0152-6 · Cited by 3,298 · see citing works
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works
Labeling images with a computer game
von Ahn, L., & Dabbish, L. (2004) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/985692.985733 · Cited by 2,230 · see citing works
Beyond PASCAL: A benchmark for 3D object detection in the wild
Yu, X., Mottaghi, R., & Savarese, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836101 · Cited by 727 · see citing works
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Su, H., Qi, C. R., Li, Y., & Guibas, L. J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.308 · Cited by 680 · see citing works
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., & Shet, V. (2013) · arXiv · doi:10.48550/arxiv.1312.6082 · Cited by 582 · see citing works
Deep Image: Scaling up Image RecognitionPreprint
Wu, R., Yan, S., Shan, Y., Dang, Q., & Sun, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.02876 · Cited by 345 · see citing works
Virtual and Real World Adaptation for Pedestrian Detection
Vázquez, D., López, A. M., Marín, J., Ponsa, D., & Gerónimo, D. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.163 · Cited by 177 · see citing works
From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains
Sun, B., & Saenko, K. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.82 · Cited by 156 · see citing works
Back to the Future: Learning Shape Models from 3D CAD Data
Stark, M. M., Goesele, M., & Schiele, B. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.106 · Cited by 136 · see citing works
Learning scene-specific pedestrian detectors without real data
Hattori, H., Boddeti, V. N., Kitani, K., & Takeo Kanade, A. (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.2015.7299006 · Cited by 134 · see citing works
Correlation Filters for Object Alignment
Boddeti, V. N., Takeo Kanade, A., & Kumar, B. V. K. V. (2013) · 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.2013.297 · Cited by 126 · see citing works
Heimerl, K., Gawalt, B., Chen, K., Parikh, T. S., & Hartmann, B. (2012) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208619 · Cited by 107 · see citing works
Streetscenes: towards scene understanding in still images
Poggio, T., & Bileschi, S. (2006) · Cited by 67 · see citing works
Ontological supervision for fine grained classification of Street View storefronts
Movshovitz-Attias, Y., Yu, Q., Stumpe, M. C., Shet, V. D., Arnoud, S., & Yatziv, 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.2015.7298778 · Cited by 66 · see citing works
Self-citationWhat is Holding Back Convnets for Detection?Preprint
Pepik, B., Benenson, R., Ritschel, T., & Schiele, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.02844 · Cited by 21 · see citing works
3D Pose-by-Detection of Vehicles via Discriminatively Reduced Ensembles of Correlation Filters
Movshovitz-Attias, Y., Sheikh, Y. A., Boddeti, V. N., & Wei, Z. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.53 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
1 of the 25 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Fatahalian, K.: Enolving the real-time graphics pipeline for micropolygon render- ing. Ph.D. thesis, Stanford University (2011)
- Law, E., Settles, B., Snook, A., Surana, H., Von Ahn, L., Mitchell, T.: Human computation for attribute and attribute value acquisition. In: Proceedings of the First Workshop on Fine-Grained Visual Categorization (FGVC) (2011)
Cited by
Sorted by citations
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124 · see citing works
“More recently, 3D models have been used to augment training data to aid transferring deep neural networks between datasets and prevent over-fitting on small datasets for tasks like viewpoint estimation [43] and object detection [45], [31].”
MethodSynthetic data in machine learning for medicine and healthcare
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00751-8 · Cited by 728 · see citing works
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
Dwibedi, D., Misra, I., & Hebert, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.146 · Cited by 687 · see citing works
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_43 · Cited by 564 · see citing works
Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes
Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., & Rother, C. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1070-x · Cited by 434 · see citing works
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 434 · see citing works
Modeling Visual Context Is Key to Augmenting Object Detection Datasets
Dvornik, N., Mairal, J., & Schmid, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_23 · Cited by 269 · see citing works
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
Zhang, Y., Song, S., Yumer, E., Savva, M., Lee, J., Jin, H., & Funkhouser, T. A. (2017) · 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.2017.537 · Cited by 251 · see citing works
On Pre-trained Image Features and Synthetic Images for Deep Learning
Hinterstoißer, S., Lepetit, V., Wohlhart, P., & Konolige, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_42 · Cited by 232 · see citing works
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., & Brox, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1082-6 · Cited by 191 · see citing works
Next-generation deep learning based on simulators and synthetic data
de Melo, C. M., Torralba, A. B., Guibas, L. J., DiCarlo, J. J., Chellappa, R., & Hodgins, J. K. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.11.008 · Cited by 173 · see citing works
Spatial Fusion GAN for Image Synthesis
Zhan, F., Zhu, H., & Lu, S. (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.2019.00377 · Cited by 160 · see citing works
Photorealistic Image Synthesis for Object Instance Detection
Hodaň, T., Vineet, V., Gal, R., Shalev, E., Hanzelka, J., Connell, T., Urbina, P., Sinha, S. N., & Guenter, B. K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803821 · Cited by 145 · see citing works
Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization
Li, R., Yuan, Y., Zhang, W., & Yuan, Y. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12351 · Cited by 140 · see citing works
Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection
Sundermeyer, M., Márton, Z.-C., Durner, M., & Triebel, R. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01243-8 · Cited by 148 · see citing works
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Wen, B., Mitash, C., Ren, B., & Bekris, K. E. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341314 · Cited by 137 · see citing works
Afifi, M., & Brown, M. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00033 · Cited by 123 · see citing works
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
Fan, Z., Zhu, Y., He, Y., Sun, Q. L., Liu, H., & He, J. Y. D. (2022) · ACM Computing Surveys · doi:10.1145/3524496 · Cited by 119 · see citing works
An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Detection
Hinterstoißer, S., Pauly, O., Heibel, H., Marek, M., & Bokeloh, M. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00340 · Cited by 115 · see citing works
Mitash, C., Bekris, K. E., & Boularias, A. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202206 · Cited by 102 · see citing works
Deep domain adaptation based video smoke detection using synthetic smoke images
Gao, X., Zhang, Y., Zhang, Q., Lin, G., & Wang, J. (2017) · Fire Safety Journal · doi:10.1016/j.firesaf.2017.08.004 · Cited by 97 · see citing works
Paulin, G., & Ivašić-Kos, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10358-3 · Cited by 100 · see citing works
On the Importance of Visual Context for Data Augmentation in Scene Understanding
Dvornik, N., Mairal, J., & Schmid, C. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2961896 · Cited by 89 · see citing works
Jiang, C., Qi, S., Zhu, Y., Huang, S., Lin, J., Yu, L.-F., Terzopoulos, D., & Zhu, S. C. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1103-5 · Cited by 80 · see citing works
Eversberg, L., & Lambrecht, J. (2021) · Sensors · doi:10.3390/s21237901 · Cited by 74 · see citing works
Pre-Training Without Natural Images
Kataoka, H., Okayasu, K., Matsumoto, A., Yamagata, E., Yamada, R., Inoue, N., Nakamura, A., & Satoh, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01555-8 · Cited by 51 · see citing works
Image Compositing for Segmentation of Surgical Tools Without Manual Annotations
García-Peraza-Herrera, L. C., Fidon, L., D’Ettorre, C., Stoyanov, D. V., Vercauteren, T. K., & Sebastien Ourselin, A. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3057884 · Cited by 50 · see citing works
Neural Network Generalization: The Impact of Camera Parameters
Liu, Z., Lian, T., Farrell, J., & Wandell, B. A. (2020) · IEEE Access · doi:10.1109/access.2020.2965089 · Cited by 43 · see citing works
A powerful image synthesis and semi-supervised learning pipeline for site-specific weed detection
Hu, C., Thomasson, J. A., & Bagavathiannan, M. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106423 · Cited by 42 · see citing works
Tang, S., Huang, H., Zhang, Y., Yao, M., Li, X., Xie, L., & Wang, W. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105076 · Cited by 39 · see citing works
Incremental Object Learning From Contiguous Views
Stojanov, S., Mishra, S., Thai, N. A., Dhanda, N., Humayun, A., Yu, C., Smith, L. B., & Rehg, J. M. (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.2019.00898 · Cited by 35 · see citing works
Computer Vision – ECCV 2016 Workshops
Lin, X., Casas, J. R., & Pardàs, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8 · Cited by 34 · see citing works
Vision-Based Gesture Recognition in Human-Robot Teams Using Synthetic Data
de Melo, C. M., Rothrock, B., Gurram, P. K., Ulutan, O., & Manjunath, B. S. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340728 · Cited by 33 · see citing works
Yolov5s-CA: An Improved Yolov5 Based on the Attention Mechanism for Mummy Berry Disease Detection
Obsie, E. Y., Qu, H., Zhang, Y., Annis, S. L., & Drummond, F. A. (2022) · Agriculture · doi:10.3390/agriculture13010078 · Cited by 33 · see citing works
Towards Fully-Synthetic Training for Industrial Applications
Mayershofer, C., Ge, T., & Fottner, J. (2021) · Springer eBooks · doi:10.1007/978-981-33-4359-7_53 · Cited by 31 · see citing works
Semiautomatic Labeling for Deep Learning in Robotics
Gregorio, D. D., Tonioni, A., Palli, G., & Stefano, L. D. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2938316 · Cited by 30 · see citing works
DD-Aug: A Knowledge-to-Image Synthetic Data Augmentation Pipeline for Industrial Defect Detection
Ren, W., Song, K., Chen, C.-Y., Chen, Y., Hong, J., Fan, M., Ouyang, X., Zhu, Y., & Xiao, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3495786 · Cited by 29 · see citing works
Domain randomization for neural network classification
Valtchev, S. Z., & Wu, J. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00455-5 · Cited by 29 · see citing works
Interactive Process Automation based on lightweight object detection in manufacturing processes
Mangat, A. S., Mangler, J., & Rinderle‐Ma, S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103482 · Cited by 29 · see citing works
Synthetic training data generation for deep learning based quality inspection
Gutierrez, P., Luschkova, M., Cordier, A., Shukor, M., Schappert, M., & Dahmen, T. (2021) · International Conference on Quality Control by Artificial Vision (QCAV) · doi:10.1117/12.2586824 · Cited by 28 · see citing works
Shi, M., Zhao, T., West, S. J., Desjardins, A. E., Vercauteren, T. K., & Xia, W. (2022) · Photoacoustics · doi:10.1016/j.pacs.2022.100351 · Cited by 27 · see citing works
Hinterstoißer, S., Pauly, O., Heibel, H., Marek, M., & Bokeloh, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09967 · Cited by 26 · see citing works
Robot obstacle avoidance system using deep reinforcement learning
Zhu, X., Liang, Y., Sun, H., Wang, X., & Ren, B. (2021) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-06-2021-0127 · Cited by 23 · see citing works
Specular-to-Diffuse Translation for Multi-view Reconstruction
Wu, S., Huang, H. F., Portenier, T., Sela, M., Cohen‐Or, D., Kimmel, R., & Zwicker, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_12 · Cited by 23 · see citing works
Fast Synthetic Dataset for Kitchen Object Segmentation in Deep Learning
Sagues-Tanco, R., Benages-Pardo, L., Nicolás, G. L., & Llorente, S. (2020) · IEEE Access · doi:10.1109/access.2020.3043256 · Cited by 22 · see citing works
Moonen, S., Vanherle, B., de Hoog, J., Bourgana, T., Bey-Temsamani, A., & Michiels, N. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00065 · Cited by 21 · see citing works
Deep learning‐based vehicle detection with synthetic image data
Wang, Y., Deng, W., Liu, Z., & Wang, J. (2019) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5365 · Cited by 21 · see citing works
Physics-based scene-level reasoning for object pose estimation in clutter
Mitash, C., Boularias, A., & Bekris, K. E. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919846551 · Cited by 20 · see citing works
Hein, J., Cavalcanti, N., Suter, D., Zingg, L., Carrillo, F., Calvet, L., Farshad, M., Navab, N., Pollefeys, M., & Fürnstahl, P. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103613 · Cited by 19 · see citing works
SynPo-Net—Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training
Su, Y., Rambach, J. R., Pagani, A., & Stricker, D. (2021) · Sensors · doi:10.3390/s21010300 · Cited by 19 · see citing works
Son, G.-J., Kwak, D.-H., Park, M., Kim, Y., & Jung, H. (2021) · Sustainability · doi:10.3390/su132413834 · Cited by 18 · see citing works
Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks
Roveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Shahid, A. A., Bucca, G., & Piga, D. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01558-0 · Cited by 17 · see citing works
Artificial and beneficial – Exploiting artificial images for aerial vehicle detection
Weber, I., Bongartz, J., & Roscher, R. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.02.015 · Cited by 16 · see citing works
da Silva, M. A. F. M., Seoane, A., Mures, O. A., López, A. M., & Iglesias-Guitián, J. A. (2025) · The Visual Computer · doi:10.1007/s00371-025-03811-1 · Cited by 15 · see citing works
Synthetically Trained 3D Visual Tracker of Underwater Vehicles
Koreitem, K., Li, J., Karp, I., Manderson, T., Shkurti, F., & Dudek, G. (2018) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2018.8604597 · Cited by 15 · see citing works
Image Synthesis Pipeline for CNN-Based Sensing Systems
Frolov, V. A., Faizov, B., Shakhuro, V., Санжаров, В., Konushin, A., Galaktionov, V. A., & Voloboy, A. G. (2022) · Sensors · doi:10.3390/s22062080 · Cited by 14 · see citing works
Minami, S., Hayashi, Y., Wu, S., Fukumizu, K., Sugisawa, H., Ishii, M., Kuwajima, I., Shiratori, K., & Yoshida, R. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01606-5 · Cited by 16 · see citing works
Adaptive Composition GAN towards Realistic Image SynthesisPreprint
Zhan, F., Huang, J., & Lu, S. (2019) · arXiv (Cornell University) · Cited by 13 · see citing works
Gaspar, F. L., Carreira, D., Rodrigues, N. M. M., Miragaia, R., Ribeiro, J., Costa, P., & Pereira, A. M. D. J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110019 · Cited by 12 · see citing works
Training my car to see using virtual worlds
López, A. M., Villalonga, G., Sellart, L., Ros, G., Vázquez, D., Xu, J., Marín, J., & Mozafari, A. S. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.07.007 · Cited by 11 · see citing works
Improving object detector training on synthetic data by starting with a strong baseline methodology
Ruis, F., Liezenga, A. M., Heslinga, F. G., Ballan, L., Eker, T. A., den Hollander, R. J. M., van Leeuwen, M. C., Dijk, J., & Huizinga, W. (2024) · Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications · doi:10.1117/12.3013441 · Cited by 11 · see citing works
PennSyn2Real: Training Object Recognition Models Without Human Labeling
Nguyen, T., Miller, I. D., Cohen, A., Thakur, D., Guru, A., Prasad, S., Taylor, C. J., Chaudhari, P., & Kumar, V. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3070249 · Cited by 11 · see citing works
Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection
Yun, W., Kim, T., Lee, J., Kim, J., & Kim, J. (2021) · IEEE Access · doi:10.1109/access.2021.3051964 · Cited by 11 · see citing works
Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery
Jareño, S. J. N., van Helden, D. P., Mirkes, E. M., Tyukin, I., & Allison, P. M. (2021) · Entropy · doi:10.3390/e23091140 · Cited by 10 · see citing works
Military Vehicle Recognition with Different Image Machine Learning Techniques
Legendre, D., & Vankka, J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-59506-7_19 · Cited by 9 · see citing works
Image Compositing for Segmentation of Surgical Tools Without Manual Annotations
García-Peraza-Herrera, L. C., Fidon, L., D’Ettorre, C., Stoyanov, D. V., Vercauteren, T. K., & Sebastien Ourselin, A. (2021) · UCL Discovery (University College London) · Cited by 9 · see citing works
Xie, D., Zhang, Y., Tian, X., Xu, L., Duan, L., & Tian, L. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129127 · Cited by 8 · see citing works
Generation of synthetic training data for object detection in piles
Buls, E., Kadiķis, R., Cacurs, R., & Ārents, J. (2019) · International Conference on Machine Vision · doi:10.1117/12.2523203 · Cited by 9 · see citing works
Li, B., Yuan, X., & Shi, M. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.1.013016 · Cited by 7 · see citing works
Synthetic Thermal Image Generation for Human-Machine Interaction in Vehicles
Blythman, R., Elrasad, A., O’Connell, E., Kielty, P., O'Byrne, M., Moustafa, M., Ryan, C., & Lemley, J. (2020) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex48832.2020.9123135 · Cited by 7 · see citing works
Li, J., Götvall, P.-L., Provost, J., & Åkesson, K. (2019) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2019.8869484 · Cited by 7 · see citing works
Capsule Based Image Synthesis for Interior Design Effect Rendering
Yang, F. H., Lu, Z., Qiu, G., Lin, J., & Zhang, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_12 · Cited by 7 · see citing works
State evaluation method for complex task network models
Liang, X., Bao, D., & Yang, Z. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119796 · Cited by 6 · see citing works
Learning deep domain-agnostic features from synthetic renders for industrial visual inspection
Abubakr, A. G., Jovančević, I., Mokhtari, N. I., Abdallah, H. B., & Orteu, J. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.051604 · Cited by 6 · see citing works
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Abhimanyu, F., Zodage, T., Thillaivasan, U., Lai, X., Chakwate, R., Santillán, J. D., Oti, E., Zhao, M. L., Boirum, R., Choset, H., et al. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981860 · Cited by 7 · see citing works
Underwater Communication Using Full-Body Gestures and Optimal Variable-Length Prefix Codes
Koreitem, K., Li, J., Karp, I., Manderson, T., & Dudek, G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793644 · Cited by 7 · see citing works
Low-Quality Rendering-Driven 6D Object Pose Estimation from Single RGB Image
Zuo, G., Zhang, C., Liu, H., & Gong, D. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207286 · Cited by 6 · see citing works
Koporec, G., & Perš, J. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00310 · Cited by 6 · see citing works
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesPreprint
Sundermeyer, M., Márton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.01275 · Cited by 6 · see citing works
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Mikami, H., Fukumizu, K., Murai, S., Suzuki, S., Kikuchi, Y., Suzuki, T., Maeda, S., & Hayashi, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26409-2_29 · Cited by 13 · see citing works
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Preprint
Mikami, H., Fukumizu, K., Murai, S., Suzuki, S., Kikuchi, Y., Suzuki, T., Maeda, S., & Hayashi, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.11018 · Cited by 5 · see citing works
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Ledda, E., Putzu, L., Delussu, R., Loddo, A., & Fumera, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89131-2_5 · Cited by 4 · see citing works
Information Gain Sampling for Active Learning in Medical Image Classification
Mehta, R., Shui, C., Nichyporuk, B., & Arbel, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16749-2_13 · Cited by 4 · see citing works
Human–Robot Labeling Framework to Construct Multitype Real-World Datasets
Elsharkawy, A., & Kim, M. S. (2022) · IEEE Access · doi:10.1109/access.2022.3229864 · Cited by 4 · see citing works
On learning deep domain-invariant features from 2D synthetic images for industrial visual inspection
Abubakr, A. G., Jovančević, I., Mokhtari, N. I., Abdallah, H. B., & Orteu, J. (2021) · International Conference on Quality Control by Artificial Vision (QCAV) · doi:10.1117/12.2589040 · Cited by 4 · see citing works
Towards fully synthetic training of 3D indoor object detectors: Ablation study
Džijan, M., Grbić, R., Vidović, I., & Cupec, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120723 · Cited by 4 · see citing works
Self-Supervised Learning of Color Constancy
Ernst, M., López, F. M., Aubret, A., Fleming, R. W., & Triesch, J. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644375 · Cited by 3 · see citing works
Autonomous Vision-Based Navigation around Asteroids Using Convolutional Neural Networks
der Heijden, L. V., Mooij, E., & Woicke, S. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-1699 · Cited by 3 · see citing works
Hafez, N., Dietrich, V., & Röhrl, S. (2020) · Mechanisms and machine science · doi:10.1007/978-3-030-48989-2_54 · Cited by 3 · see citing works
Efficient Realistic Data Generation Framework Leveraging Deep Learning-Based Human Digitization
Symeonidis, C., Nousi, P., Tosidis, P., Tsampazis, K., Passalis, N., Tefas, A., & Nikolaidis, N. (2021) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-80568-5_23 · Cited by 4 · see citing works
Proceedings of the 22nd Engineering Applications of Neural Networks Conference
Charalampos, S., Nousi, P., Tosidis, P., Tsampazis, K., Passalis, N., Tefas, A., & Nikolaidis, N. (2021) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-80568-5 · Cited by 3 · see citing works
Online Learning Problems and the Principal’s Efforts to Overcome the Problems
Asha, L. (2021) · AL-ISHLAH Jurnal Pendidikan · doi:10.35445/alishlah.v13i2.932 · Cited by 3 · see citing works
Synthetic learning for primitive-based building model reconstruction from point clouds
Li, Z., & Shan, J. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.12.012 · Cited by 2 · see citing works
Fine-Grained Vehicle Representations for Autonomous Driving
Barowski, T., Szczot, M., & Houben, S. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569930 · Cited by 2 · see citing works
On the use of simulated data for target recognition and mission planning
Heslinga, F. G., Fokkinga, E. P., Eker, T. A., Liezenga, A. M., den Hollander, R. J. M., Oppeneer, V. O., van Heteren, A. M., Vossen, R. V., Kuijf, H. J., Sande, J. V., et al. (2024) · SPIE Conference on Artificial Intelligence for Security and Defence Applications · doi:10.1117/12.3031638 · Cited by 2 · see citing works
LEARNING WITH REAL-WORLD AND ARTIFICIAL DATA FOR IMPROVED VEHICLE DETECTION IN AERIAL IMAGERY
Weber, I., Bongartz, J., & Roscher, R. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-917-2020 · Cited by 2 · see citing works
Role of Simulated Lidar Data for Training 3D Deep Learning Models: An Exhaustive Analysis
Lohani, B., Khan, P., Kumar, V., & Gupta, S. D. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-01905-2 · Cited by 1 · see citing works
Moonen, S. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1158 · Cited by 1 · see citing works
Variable photorealistic image synthesis for training dataset generation
Санжаров, В., Frolov, V. A., & Voloboy, A. G. (2020) · International Scientific Conference on Computing in Physics and Technology (CPT) · doi:10.30987/conferencearticle_5fce27723872e5.04814843 · Cited by 1 · see citing works
Санжаров, В., Frolov, V. A., & Voloboy, A. G. (2020) · doi:10.33965/cgv2020_202011l014 · Cited by 1 · see citing works
- Authors
- Movshovitz-Attias, Y., Kanade, T., & Sheikh, Y.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Movshovitz-Attias, Yair
- ORCID
- Sheikh, Yaser
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 202-217
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2315410813
- Cited by
- 118
- References
- 25
- Citations by year
- 2025: 8 · 2024: 7 · 2023: 11 · 2022: 12 · 2021: 26 · 2020: 16 · 2019: 21 · 2018: 9 · 2017: 7 · 2016: 1
- FWCI
- 6.83
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- photorealistic rendering, synthetic data, domain adaptation, visual learning
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