The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
How Useful Is Photo-Realistic Rendering for Visual Learning?
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
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
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
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
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
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
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 3,202
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
Labeling images with a computer game
von Ahn, L., & Dabbish, L. (2004) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985733 · Cited by 2,234
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 741
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 682
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
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
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
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 160
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
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
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
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
Streetscenes: towards scene understanding in still images
Poggio, T., & Bileschi, S. (2006) · Cited by 67
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
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
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
Self-citation
Research works printed but not linked: 2
- Fatahalian, K.: Enolving the real-time graphics pipeline for micropolygon rendering. 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
“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
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 709
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
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 447
Metadata
- 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
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
