Synthetic Examples Improve Generalization for Rare Classes
Open accessAbstract
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision systems struggle to categorize objects they have seen only rarely during training, and collecting a sufficient number of training examples of rare events is often challenging and expensive, and sometimes outright impossible. We explore in depth an approach to this problem: complementing the few available training images with ad-hoc simulated data.Our testbed is animal species classification, which has a real-world long-tailed distribution. We present two natural world simulators, and analyze the effect of different axes of variation in simulation, such as pose, lighting, model, and simulation method, and we prescribe best practices for efficiently incorporating simulated data for real-world performance gain. Our experiments reveal that synthetic data can considerably reduce error rates for classes that are rare, that as the amount of simulated data is increased, accuracy on the target class improves, and that high variation of simulated data provides maximum performance gain.
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Computer vision can be used to classify species from camera traps, supporting a real-time, less labor-intensive species count.
“For instance, camera traps take photos automatically whenever a motion sensor is activated—computer vision can be used to classify the species that pass by, supporting a real-time, less labor-intensive species count [64,585,643].”
From Tackling climate change with machine learning (2023) · cites this paper as [64]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Self-citationImageNet 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
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556 · see citing works
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,360 · see citing works
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Shutao Li, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633969 · Cited by 4,945 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works
Class-Balanced Loss Based on Effective Number of Samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00949 · Cited by 3,094 · see citing works
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088 · cited 1× · see citing works
“Imbalanced datasets often lead to bias in algorithm performance toward well-represented classes [23].”
BackgroundOne-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works
Self-citationSpeed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.351 · Cited by 2,743 · see citing works
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259 · see citing works
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-67361-5_40 · Cited by 2,254 · see citing works
Self-citationPlaying for Data: Ground Truth from Computer Games
Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_7 · Cited by 1,928 · see citing works
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
Self-citationLearning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (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.2017.241 · Cited by 1,831 · see citing works
The foundations of cost-sensitive learning
Elkan, C. (2001) · Cited by 1,812 · see citing works
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603 · see citing works
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training
Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_18 · Cited by 1,464 · see citing works
DRAW: A Recurrent Neural Network For Image GenerationPreprint
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04623 · Cited by 1,394 · see citing works
Semantic Scene Completion from a Single Depth Image
Song, S., Yu, F., Zeng, A., Chang, A. X., Savva, M., & 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.2017.28 · Cited by 1,244 · see citing works
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230 · see citing works
VirtualWorlds as Proxy for Multi-object Tracking Analysis
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.470 · Cited by 1,079 · see citing works
Learning from Synthetic Humans
Varol, G., Romero, J., Martín, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (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.2017.492 · Cited by 915 · see citing works
Leafsnap: A Computer Vision System for Automatic Plant Species Identification
Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_36 · Cited by 825 · see citing works
Low-Shot Visual Recognition by Shrinking and Hallucinating Features
Hariharan, B., & Girshick, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.328 · Cited by 824 · see citing works
Luan, F., Paris, S., Shechtman, E., & Bala, K. (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.2017.740 · Cited by 729 · see citing works
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679 · see citing works
Self-citationAI2-THOR: An Interactive 3D Environment for Visual AI
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05474 · Cited by 578 · see citing works
Learning Category-Specific Mesh Reconstruction from Image Collections
Kanazawa, A., Tulsiani, S., Efros, A. A., & Malik, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_23 · Cited by 540 · see citing works
Some Improvements on Deep Convolutional Neural Network Based Image Classification
Howard, A. G. (2013) · arXiv · doi:10.48550/arxiv.1312.5402 · Cited by 356 · see citing works
Virtual Worlds as Proxy for Multi-Object Tracking AnalysisPreprint
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06457 · Cited by 302 · see citing works
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Wang, Y.-X., & Hebert, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_37 · Cited by 296 · see citing works
Building Generalizable Agents with a Realistic and Rich 3D Environment
Wu, Y., Wu, Y., Gkioxari, G., & Tian, Y. (2018) · CaltechAUTHORS (California Institute of Technology) · doi:10.48550/arxiv.1801.02209 · Cited by 272 · see citing works
MINOS: Multimodal Indoor Simulator for Navigation in Complex EnvironmentsPreprint
Savva, M., Chang, A. X., Dosovitskiy, A., Funkhouser, T., & Koltun, V. (2017) · arXiv.org · doi:10.48550/arxiv.1712.03931 · Cited by 262 · see citing works
Cui, Y., Zhou, F., Lin, Y., & Belongie, S. (2016) · 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.2016.130 · Cited by 232 · see citing works
Generating images with recurrent adversarial networksPreprint
Im, D. J., Kim, C. D., Jiang, H., & Memisevic, R. (2016) · arXiv.org · doi:10.48550/arxiv.1602.05110 · Cited by 232 · see citing works
A Bayesian Data Augmentation Approach for Learning Deep ModelsPreprint
Tran, T. B., Pham, T. T., Carneiro, G., Palmer, L. J., & Reid, I. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.10564 · Cited by 197 · see citing works
Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T. S., & Wang, Y. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3129396 · Cited by 190 · see citing works
Ji, S., Shen, Y., Lü, M., & Zhang, Y. (2019) · Remote Sensing · doi:10.3390/rs11111343 · Cited by 168 · see citing works
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Horn, G. V., & Perona, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01450 · Cited by 155 · see citing works
Self-citationWhat Shape Are Dolphins? Building 3D Morphable Models from 2D Images
Cashman, T. J., & Fitzgibbon, A. W. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.68 · Cited by 143 · 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
Procedural Generation of Videos to Train Deep Action Recognition Networks
de Souza, C. R., Gaidon, A., Cabon, Y., & López, A. M. (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.2017.278 · Cited by 131 · see citing works
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Cui, Y., Jia, M., Lin, T.-Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.05555 · Cited by 128 · 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
Object Detection Using Deep CNNs Trained on Synthetic ImagesPreprint
Rajpura, P., Bojinov, H., & Hegde, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06782 · Cited by 84 · see citing works
Bondi, E., Dey, D., Kapoor, A., Piavis, J., Shah, S. C., Fang, F., Dilkina, B., Hannaford, R., Iyer, A., Joppa, L., et al. (2018) · ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3209811.3209880 · Cited by 72 · see citing works
Self-citationSyn2Real: A New Benchmark forSynthetic-to-Real Visual Domain AdaptationPreprint
Peng, X., Usman, B., Saito, K., Kaushik, N., Hoffman, J., & Saenko, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09755 · Cited by 47 · see citing works
The iNaturalist Challenge 2017 DatasetPreprint
Horn, G. V., Aodha, O. M., Song, Y., Shepard, A. D., Adam, H., Perona, P., & Belongie, S. (2017) · arXiv (Cornell University) · Cited by 46 · see citing works
Self-citationWhat Is Holding Back Convnets for Detection?
Pepik, B., Benenson, R., Ritschel, T., & Schiele, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_43 · Cited by 45 · see citing works
Automatically identifying wild animals in camera trap images with deep learning.Preprint
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Packer, C., & Clune, J. (2017) · arXiv (Cornell University) · Cited by 37 · 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
Efficient generation of image chips for training deep learning algorithms
Han, S., Fafard, A., Kerekes, J. P., Gartley, M. G., Ientilucci, E. J., Savakis, A. E., Law, C., Parhan, J., Turek, M., Fieldhouse, K., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2261702 · Cited by 21 · see citing works
What 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
The iNaturalist Species Classification and Detection DatasetPreprint
Horn, G. V., Aodha, O. M., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06642 · Cited by 18 · see citing works
Self-citationNorouzzadeh, M. S., Nguyen, A., Kosmala, M., et al. (2017) · arXiv (Cornell University) · doi:10.35099/aurora-7 · Cited by 12 · see citing works
Animated 3D Creatures from Single-view Video by Skeletal Sketching
Reinert, B., Ritschel, T., & Seidel, H. (2016) · MPG.PuRe (Max Planck Society) · doi:10.5555/3076132.3076159 · Cited by 10 · see citing works
Low-Shot Learning From Imaginary 3D Model
Pahde, F., Puscas, M., Wolff, J., Klein, T., Sebe, N., & Nabi, M. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00109 · Cited by 9 · see citing works
Low-Shot Learning from Imaginary 3D ModelPreprint
Pahde, F., Puscas, M., Wolff, J., Klein, T., Sebe, N., & Nabi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.01868 · Cited by 1 · see citing works
Flagged first, then by citations
4 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- [21][21] E. Bondi, D. Dey, A. Kapoor, J. Piavis, S. Shah, F. Fang, B. Dilkina, R. Hannaford, A. Iyer, L. Joppa, et al. Airsimw: A simulation environment for wildlife conservation with uavs. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies , page 40. ACM, 2018. 3 -
- [35][35] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems , pages 2672-2680, 2014. 1, 3, 8 -
- [74][74] T. Tieleman and G. Hinton. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning , 4(2):26-31, 2012. 5 -
- [81][81] Y. Z. S. Q. Z. X. T. S. K. Y. W. A. Y. Weichao Qiu, Fangwei Zhong. Unrealcv: Virtual worlds for computer vision. ACM Multimedia Open Source Software Competition , 2017. 3 -
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Vélez, J., McShea, W. J., Shamon, H., Castiblanco‐Camacho, P. J., Tabak, M. A., Chalmers, C., Fergus, P., & Fieberg, J. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14044 · Cited by 139 · see citing works
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Beery, S., Wu, G., Rathod, V., Votel, R., & Huang, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01309 · Cited by 140 · see citing works
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Xu, J., Yuan, C., Gu, J., Liu, J., An, J., & Kong, Q. (2022) · Structural Health Monitoring · doi:10.1177/14759217221122318 · Cited by 51 · see citing works
Simões, F., Bouveyron, C., & Precioso, F. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102095 · Cited by 50 · see citing works
Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias
Paproki, A., Salvado, O., & Fookes, C. (2024) · ACM Computing Surveys · doi:10.1145/3663759 · Cited by 50 · see citing works
The iWildCam 2020 Competition DatasetPreprint
Beery, S., Cole, E., & Gjoka, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10340 · Cited by 45 · see citing works
Self-citationGenerative AI as a tool to accelerate the field of ecology
Rafiq, K., Beery, S., Palmer, M. S., Harchaoui, Z., & Abrahms, B. L. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02623-1 · Cited by 46 · see citing works
Self-citationIndoor Synthetic Data Generation: A Systematic Review
Schieber, H., Demir, K. C., Kleinbeck, C., Yang, S. H., & Roth, D. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103907 · Cited by 42 · see citing works
Villon, S., Iovan, C., Mangeas, M., & Vigliola, L. (2022) · Sensors · doi:10.3390/s22020497 · Cited by 40 · see citing works
Elephants and algorithms: a review of the current and future role of AI in elephant monitoring
Brickson, L. L., Zhang, L., Vollrath, F. W., Douglas‐Hamilton, I., & Titus, A. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0367 · Cited by 44 · see citing works
Gadot, T., Istrate, Ș., Kim, H., Morris, D., Beery, S., Birch, T., & Ahumada, J. (2024) · IET Computer Vision · doi:10.1049/cvi2.12318 · Cited by 35 · see citing works
Self-citationNorman, D. L., Bischoff, P. H., Wearn, O. R., Ewers, R. M., Rowcliffe, J. M., Evans, B. C., Sethi, S. S., Chapman, P. M., & Freeman, R. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14031 · Cited by 32 · see citing works
From generalized zero-shot learning to long-tail with class descriptors
Samuel, D., Atzmon, Y., & Chechik, G. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00033 · Cited by 32 · see citing works
Uncertainty-Guided Boundary Learning for Imbalanced Social Event Detection
Ren, J., Peng, H. M., Jiang, L., Liu, Z., Wu, J. X., Yu, Z., & Yu, P. S. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3324510 · Cited by 29 · see citing works
Multispecies facial detection for individual identification of wildlife: a case study across ursids
Clapham, M., Miller, E., Nguyen, M., & HORN, R. C. V. (2022) · Mammalian Biology · doi:10.1007/s42991-021-00168-5 · Cited by 28 · see citing works
Getting the bugs out of AI : Advancing ecological research on arthropods through computer vision
Schneider, S., Taylor, G. W., Kremer, S. C., & Fryxell, J. M. (2023) · Ecology Letters · doi:10.1111/ele.14239 · Cited by 30 · see citing works
Xu, L.-Q., Liu, H., Xiao, B., Luo, X., DharmarajVeeramani, & Zhu, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102699 · Cited by 27 · see citing works
Contour-Based Wild Animal Instance Segmentation Using a Few-Shot Detector
Tang, J., Zhao, Y., Feng, L., & Zhao, W. (2022) · Animals · doi:10.3390/ani12151980 · Cited by 26 · see citing works
Surveying the deep: A review of computer vision in the benthos
Trotter, C., Griffiths, H. J., & Whittle, R. J. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102989 · Cited by 25 · see citing works
Rafiei, A., Rad, M. G., Sikora, A. N., & Kamaleswaran, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107749 · Cited by 24 · see citing works
ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification
Kulits, P., Wall, J., Bedetti, A., Henley, M. D., & Beery, S. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3460112.3471947 · Cited by 24 · see citing works
Self-citationKhayatian, F., Nagy, Z., & Bollinger, A. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111334 · Cited by 24 · see citing works
Schwartz, S. T., & Alfaro, M. E. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13712 · Cited by 24 · see citing works
Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age
Pringle, S., Dallimer, M., Goddard, M. A., Goff, L. K. L., Hart, E., Langdale, S. J., Fisher, J. C., Abad, S.-A., Ancrenaz, M., Angeoletto, F. H. S., et al. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02704-9 · Cited by 23 · see citing works
A gentle introduction to computer vision‐based specimen classification in ecological datasets
Blair, J. D., Gaynor, K. M., Palmer, M. S., & Marshall, K. E. (2024) · Journal of Animal Ecology · doi:10.1111/1365-2656.14042 · Cited by 23 · see citing works
Opportunities for synthetic data in nature and climate finance
Tkachenko, N. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1168749 · Cited by 18 · see citing works
The Performance and Potential of Deep Learning for Predicting Species Distributions
Kellenberger, B., Winner, K., & Jetz, W. (2026) · Global Ecology and Biogeography · doi:10.1111/geb.70184 · Cited by 17 · see citing works
The iWildCam 2021 Competition DatasetPreprint
Beery, S., Agarwal, A., Cole, E., & Birodkar, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03494 · Cited by 42 · see citing works
Self-citationKshirsagar, M., Robinson, C., Yang, S., Gholami, S., Klyuzhin, I. S., Mukherjee, S., Nasır, Ortiz, A., Oviedo, F., Tanner, D., et al. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462599 · Cited by 13 · see citing works
Exploring the Sim2Real Gap using Digital Twins
Sudhakar, S., Hanzelka, J., Bobillot, J., Randhavane, T., Joshi, N., & Vineet, V. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01867 · Cited by 14 · see citing works
Self-citationHuman Body Measurement Estimation with Adversarial Augmentation
Ruiz, N., Bellver, M., Bolkart, T., Arora, A., Lin, M. C., Romero, J., & Bala, R. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00034 · Cited by 13 · see citing works
The iWildCam 2019 Challenge DatasetPreprint
Beery, S., Morris, D., & Perona, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.07617 · Cited by 12 · see citing works
Self-citationA Deep Analysis on the Role of Deep Learning Models Using Generative Adversarial Networks
Aggarwal, A., Gaba, S., Nagpal, S., & Arya, A. (2022) · Studies in big data · doi:10.1007/978-3-030-95419-2_9 · Cited by 11 · see citing works
Investigating the Sim-to-Real Generalizability of Deep Learning Object Detection Models
Rüter, J., Durak, U., & Dauer, J. C. (2024) · Journal of Imaging · doi:10.3390/jimaging10100259 · Cited by 11 · see citing works
Xu, Y., Huang, B., Luo, X., Bradbury, K., & Malof, J. M. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3172243 · Cited by 11 · see citing works
Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition
Villalonga, G., van de Weijer, J., & López, A. (2020) · Italian National Conference on Sensors · doi:10.3390/s20030583 · Cited by 10 · see citing works
Simulated Adversarial Testing of Face Recognition Models
Ruiz, N., Kortylewski, A., Qiu, W., Xie, C., Bargal, S. A., Yuille, A., & Sclaroff, S. (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/cvpr52688.2022.00411 · Cited by 9 · see citing works
Scaling Biodiversity Monitoring for the Data Age
Beery, S. (2021) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3466857 · Cited by 9 · see citing works
Self-citationRüter, J., Maienschein, T., Schirmer, S., Schopferer, S., & Torens, C. (2024) · Sensors · doi:10.3390/s24041144 · Cited by 8 · see citing works
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
Jain, S., Paudel, D. P., Danelljan, M., & Gool, L. V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00733 · Cited by 8 · see citing works
Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
Hou, X., Xu, J., Wu, J., & Xu, H. (2021) · Applied Sciences · doi:10.3390/app112412037 · Cited by 8 · see citing works
SimZSL: Zero-Shot Learning Beyond a Pre-defined Semantic Embedding Space
Atigh, M. G., Nargang, S., Keller‐Ressel, M., & Mettes, P. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02422-6 · Cited by 7 · see citing works
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
Gasperini, S., Marcos-Ramiro, A., Schmidt, M., Navab, N., Busam, B., & Tombari, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01770 · Cited by 7 · see citing works
Deep learning to extract the meteorological by‐catch of wildlife cameras
Alison, J., Payne, S. L., Alexander, J. M., Bjorkman, A. D., Clark, V. R., Gwate, O., Huntsaar, M., Iseli, E., Lenoir, J., Mann, H. M. R., et al. (2023) · Global Change Biology · doi:10.1111/gcb.17078 · Cited by 8 · see citing works
Pringle, J. S., Davies, Z. G., Goddard, M. A., Dallimer, M., Hart, E., Goff, L. K. L., & Langdale, S. J. (2023) · UK-RAS white papers · doi:10.31256/wp2023.4 · Cited by 7 · see citing works
Improving the Classification of Rare Chords With Unlabeled Data
Bortolozzo, M., Schramm, R., & Jung, C. R. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413701 · Cited by 6 · see citing works
A Shared Representation for Photorealistic Driving Simulators
Saadatnejad, S., Li, S., Mordan, T., & Alahi, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3131303 · Cited by 7 · see citing works
From Presence‐Only to Abundance Species Distribution Models Using Transfer Learning
Bourel, B., Joly, A., Servajean, M., Bettinger, S., Sanabria‐Fernández, J. A., & Mouillot, D. (2025) · Ecology Letters · doi:10.1111/ele.70177 · Cited by 5 · see citing works
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
Lehner, A., Gasperini, S., Marcos-Ramiro, A., Schmidt, M., Navab, N., Busam, B., & Tombari, F. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01914-7 · Cited by 5 · see citing works
Martiri, E. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0255-2.ch005 · Cited by 5 · see citing works
Visual blockage assessment at culverts using synthetic images to mitigate blockage-originated floods
Iqbal, U., Barthélemy, J., & Perez, P. (2023) · Journal of Hydroinformatics · doi:10.2166/hydro.2023.068 · Cited by 4 · see citing works
Adapting the Re‐ID Challenge for Static Sensors
Sundaresan, A., Parham, J. R., Crall, J., Warungu, R., Muthami, T., Miliko, J., Mwangi, M., Holmberg, J., Berger-Wolf, T. Y., Rubenstein, D. I., et al. (2025) · IET Computer Vision · doi:10.1049/cvi2.70027 · Cited by 5 · see citing works
Self-citationCV.eDNA: A hybrid approach to invertebrate biomonitoring using computer vision and DNA metabarcodingPreprint
Blair, J. D., Weiser, M. D., Siler, C. D., Kaspari, M. E., Smith, S. N., McLaughlin, J. F., & Marshall, K. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.02.610558 · Cited by 4 · see citing works
Chon, H., Oh, D., & Noh, J. (2025) · Applied Sciences · doi:10.3390/app15063042 · Cited by 3 · see citing works
Durand, S., Foucher, S., Delplanque, A., Taillon, J., & Théau, J. (2026) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.70056 · Cited by 3 · see citing works
Banerjee, S., Joshi, A., Mahajan, P., Bhattacharya, S., Kyal, S., & Mishra, T. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00169 · Cited by 3 · see citing works
Blair, J. D., Khidas, K., & Marshall, K. E. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329482 · Cited by 3 · see citing works
Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion
Liang, Y., Bhardwaj, S., & Zhou, T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00166 · Cited by 3 · see citing works
Nikseresht, G., Gady Agam, B., & Arfanakis, K. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956383 · Cited by 3 · see citing works
Generative Adversarial Networks
Pujahari, R. M., Khan, R., & Yadav, S. P. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91660-1_1 · Cited by 3 · see citing works
Synthetic Simulated Data for Construction Automation: A Review
Xu, L.-Q., Liu, H., Xiao, B., Luo, X., & Zhu, Z. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485262.054 · Cited by 2 · see citing works
Diffusion-Based Generative Regularization for Supervised Discriminative Learning
Asakura, T., Inoue, N., & Shinoda, K. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00864 · Cited by 2 · see citing works
Synthesizing Retro Game Screenshot Datasets for Sprite Detection.
Kim, C., Kim, J., & Osborn, J. (2020) · Cited by 2 · see citing works
Domain Adaptation for Rare Classes Augmented with Synthetic SamplesPreprint
Das, T., Bruintjes, R.-J., Lengyel, A., van Gemert, J. C., & Beery, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12216 · Cited by 2 · see citing works
Self-citationGetting the Bugs Out: Entomology Using Computer VisionPreprint
Schneider, S., Taylor, G., Kremer, S. C., & Fryxell, J. M. (2022) · doi:10.22541/au.166568511.18706401/v1 · Cited by 2 · see citing works
Indoor Synthetic Data Generation: A Systematic ReviewPreprint
Schieber, H., Demir, K. C., Kleinbeck, C., Yang, S. H., & Roth, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4528507 · Cited by 2 · see citing works
When fake data is a good thing – how synthetic data trains AI to solve real problems
Tewari, A. (2025) · doi:10.64628/aai.7qpu9syyp · Cited by 2 · see citing works
Speeding up image annotation and AI-model training for wildlife images
Bolduc, D., Fauteux, D., Genest, M.-A., & Legagneux, P. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103926 · Cited by 1 · see citing works
Improving Rare Classes on nuScenes LiDAR segmentation Through Targeted Domain Adaptation
Rajendran, V., Tang, C., & van Paasschen, F. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00018 · Cited by 1 · see citing works
Minimax Modifications of Linear Discriminant Analysis for Classification with Rare Classes
Bratanova, K., Kareev, I. A., & Salimov, R. (2020) · IEEE East-West Design & Test Symposium (EWDTS) · doi:10.1109/ewdts50664.2020.9224895 · Cited by 1 · see citing works
PanDA: Panoptic Data AugmentationPreprint
Liu, Y., Perona, P., & Meister, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12317 · Cited by 1 · see citing works
Self-citationLong-tail learning with attributesPreprint
Samuel, D., Atzmon, Y., & Chechik, G. (2020) · arXiv (Cornell University) · Cited by 1 · see citing works
Image-to-Image Translation of Synthetic Samples for Rare ClassesPreprint
Lanzini, E., & Beery, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12212 · Cited by 1 · see citing works
Self-citationLong-tail learning with class descriptorsPreprint
Samuel, D., Atzmon, Y., & Chechik, G. (2020) · arXiv (Cornell University)
Banerjee, S., Joshi, A., Mahajan, P., Bhattacharya, S., Kyal, S., & Mishra, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01464
Banerjee, S., Joshi, A., Ghoneim, A., Kyal, S., & Mishra, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413822
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection ProblemsPreprint
Xu, Y., Huang, B., Luo, X., Bradbury, K., & Malof, J. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15681
Self-directed training of person reidentification with synthetic data
Dant, A., Kacenjar, S. T., & Neely, R. A. (2021) · SPIE Applications of Machine Learning · doi:10.1117/12.2594745
A Proposed Solution to Problems in Learning the Knowledge Needed by Self-Driving VehiclesPreprint
Wolff, J. G. (2021) · viXra
Optimizing User Experience in Wearable Cognitive Assistance through Model Specialization
Nguyen, C. L. T., & Satyanarayanan, M. (2023) · doi:10.1145/3615592.3616850
Surveillance robot in hazardous places
Marimganti, S., Poshala, S., Redapangu, S., Rao, M., Rao, D. S. M., Padhan, D. G., Aysola, S. C., Muhamed, H., Kirola, M., & Pushkarna, G. (2025) · AIP conference proceedings · doi:10.1063/5.0262096
Rüter, J., Dauer, J. C., & Durak, U. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-10185-3_14
A Novel GAN-Based DDoS Attack Detection Method for Smart Home Networks
Ahamed, I., Rakib, A., & Aydin, M. E. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-14350-1_7
Santos, B. (2026) · Journal of Artificial Intelligence & Control Systems · doi:10.66967/jaics.2026.v2i105
Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation
Sezer, E., & Akgöl, S. (2026) · Cumhuriyet Science Journal · doi:10.17776/csj.1861928
- Authors
- Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Meister, M., Joshi, N., & Perona, P.
- Affiliations
- California Institute of Technology (US)
- Microsoft (United States) (US)
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 852-862
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2935725472
- Cited by
- 103
- References
- 78
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 11 · 2023: 15 · 2022: 18 · 2021: 23 · 2020: 11 · 2019: 3
- FWCI
- 6.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1745301)
- U.S. Geological Survey
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- few-shot learning, synthetic data augmentation, animal species classification, long-tailed distributions, endangered species detection
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