A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

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

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

Fiji: an open-source platform for biological-image analysis

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890 · 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

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · 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

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

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 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755 · see citing works

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works

Momentum Contrast for Unsupervised Visual Representation LearningPreprint

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. B. (2019) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1911.05722 · Cited by 16,250 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · 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.1993.341010 · Cited by 14,474 · see citing works

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Speeded-Up Robust Features (SURF)

Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1× · see citing works

Momentum Contrast for Unsupervised Visual Representation Learning

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works

Untitled work

(2016) · Cited by 11,679 · see citing works

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works

The graph neural network model

Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works

Biomechanics and motor control of human movement

Winter, D. A. (1990) · Choice Reviews Online · doi:10.5860/choice.28-2135 · Cited by 8,496 · see citing works

scikit-image: image processing in Python

(2014) · doi:10.7717/peerj.453 · Cited by 6,794 · see citing works

Representation Learning with Contrastive Predictive Coding

Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works

Biomechanics and Motor Control of Human Movement

Winter, D. A. (2009) · Wiley eBooks · doi:10.1002/9780470549148 · Cited by 6,162 · see citing works

Deep High-Resolution Representation Learning for Human Pose Estimation

Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00584 · Cited by 5,984 · see citing works

Large-Scale Machine Learning with Stochastic Gradient Descent

Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795 · see citing works

Stacked Hourglass Networks for Human Pose Estimation

Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634 · see citing works

OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536 · see citing works

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187 · see citing works

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

(2018) · doi:10.1109/cvpr40276.2018 · Cited by 4,769 · see citing works

Visual perception of biological motion and a model for its analysis

Johansson, G. (1973) · Perception & Psychophysics · doi:10.3758/bf03212378 · Cited by 4,547 · see citing works

2008 IEEE Conference on Computer Vision and Pattern Recognition

(2008) · doi:10.1109/cvpr12618.2008 · Cited by 4,263 · see citing works

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works

Untitled work

(2013) · Cited by 3,740 · see citing works

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664 · see citing works

DeepPose: Human Pose Estimation via Deep Neural Networks

Toshev, A., & Szegedy, C. (2014) · 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.2014.214 · Cited by 3,288 · see citing works

Docker: lightweight Linux containers for consistent development and deployment

Merkel, D. (2014) · Linux journal · Cited by 3,277 · see citing works

2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · 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.2014.471 · Cited by 2,699 · see citing works

A survey of advances in vision-based human motion capture and analysis

Moeslund, T. B., Hilton, A., & Krüger, V. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.002 · Cited by 2,546 · see citing works

Visualizing the Loss Landscape of Neural NetsPreprint

Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1712.09913 · Cited by 2,497 · see citing works

The Open Images Dataset V4

Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,460 · see citing works

Self-Training With Noisy Student Improves ImageNet Classification

Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393 · see citing works

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Baevski, A., Zhou, H., Mohamed, A., & Auli, M. (2020) · arXiv · doi:10.48550/arxiv.2006.11477 · Cited by 2,369 · see citing works

Simple Baselines for Human Pose Estimation and Tracking

Xiao, B., Wu, H., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_29 · Cited by 2,233 · see citing works

Shortcut learning in deep neural networks

Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00257-z · Cited by 2,224 · see citing works

The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalePreprint

Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00982 · Cited by 2,194 · see citing works

Contrastive Multiview Coding

Tian, Y., Krishnan, D., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_45 · Cited by 1,997 · see citing works

Learning deep representations by mutual information estimation and maximization

Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., & Bengio, Y. (2018) · arXiv · doi:10.48550/arxiv.1808.06670 · Cited by 1,991 · see citing works

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2001)

Hillman, P., Hannah, J. M., & Renshaw, D. (2001) · Cited by 1,967 · see citing works

CellProfiler 3.0: Next-generation image processing for biology

McQuin, C., Goodman, A., Chernyshev, V. S., Kamentsky, L. D., Cimini, B. A., Karhohs, K. W., Doan, M., Ding, L., Rafelski, S. M., Thirstrup, D. J., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2005970 · Cited by 1,835 · see citing works

Terrestrial animal tracking as an eye on life and planet

Kays, R., Crofoot, M. C., Jetz, W., & Wikelski, M. (2015) · Science · doi:10.1126/science.aaa2478 · Cited by 1,638 · see citing works

Neuroscience-Inspired Artificial Intelligence

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works

DensePose: Dense Human Pose Estimation in the Wild

Güler, R. A., Neverova, N., & Kokkinos, I. (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.2018.00762 · Cited by 1,496 · see citing works

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019) · Nature Protocols · doi:10.1038/s41596-019-0176-0 · Cited by 1,476 · see citing works

A Simple Yet Effective Baseline for 3d Human Pose Estimation

Martínez, J., Hossain, R., Romero, J., & Little, J. J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.288 · Cited by 1,468 · see citing works

DensePose: Dense Human Pose Estimation in the Wild.

Güler, R. A., Neverova, N., & Kokkinos, I. (2018) · UCL Discovery (University College London) · Cited by 1,421 · see citing works

Neuroscience Needs Behavior: Correcting a Reductionist Bias

Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.041 · Cited by 1,358 · see citing works

Do Better ImageNet Models Transfer Better?

Kornblith, S., Shlens, J., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00277 · Cited by 1,325 · see citing works

Joint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint

Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 1,317 · see citing works

wav2vec: Unsupervised Pre-Training for Speech Recognition

Schneider, S., Baevski, A., Collobert, R., & Auli, M. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-1873 · Cited by 1,290 · see citing works

Exploring the Limits of Weakly Supervised Pretraining

Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224 · see citing works

A guide to convolution arithmetic for deep learningPreprint

Dumoulin, V., & Visin, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07285 · Cited by 1,223 · see citing works

DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model

Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_3 · Cited by 1,191 · see citing works

Rethinking ImageNet Pre-Training

He, K., Girshick, R., & Dollár, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00502 · Cited by 1,164 · see citing works

Visualizing the Loss Landscape of Neural Nets

Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2017) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000461393 · Cited by 1,148 · see citing works

Ilastik: Interactive learning and segmentation toolkit

Sommer, C., Straehle, C., Köthe, U., & Hamprecht, F. A. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872394 · Cited by 1,145 · see citing works

Taskonomy: Disentangling Task Transfer Learning

Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00391 · Cited by 1,128 · see citing works

Variability in the analysis of a single neuroimaging dataset by many teams

(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097 · see citing works

Recent advances in deep learning for object detection

Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047 · see citing works

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T. S., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00543 · Cited by 1,023 · see citing works

Data-Efficient Image Recognition with Contrastive Predictive Coding

Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014 · see citing works

Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints

Lu, T., & O’Connor, D. J. (1999) · Journal of Biomechanics · doi:10.1016/s0021-9290(98)00158-4 · Cited by 932 · see citing works

Vision-based human motion analysis: An overview

Poppe, R. W. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.016 · Cited by 841 · see citing works

Human Pose Estimation with Iterative Error Feedback

Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805 · see citing works

Mapping Sub-Second Structure in Mouse Behavior

Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., Abraira, V. E., Adams, R. P., & Datta, S. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.031 · Cited by 760 · see citing works

Untitled work

(2009) · Cited by 701 · 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

Through-Wall Human Pose Estimation Using Radio Signals

Zhao, M., Li, T., Alsheikh, M. A., Tian, Y., Zhao, H., Torralba, A. B., & Katabi, D. (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.2018.00768 · Cited by 676 · see citing works

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity FieldsPreprint

Cao, Z., Hidalgo, G., Simon, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08008 · Cited by 672 · see citing works

Fast animal pose estimation using deep neural networks

Pereira, T., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S. ‐., Murthy, M., & Shaevitz, J. W. (2018) · Nature Methods · doi:10.1038/s41592-018-0234-5 · Cited by 659 · see citing works

JAABA: Interactive machine learning for automatic annotation of animal behavior

Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S., & Branson, K. (2013) · Nature Methods · doi:10.1038/nmeth.2281 · Cited by 644 · see citing works

Survey of spiking in the mouse visual system reveals functional hierarchy

Siegle, J. H., Jia, X., Durand, S., Gale, S. D., Bennett, C., Graddis, N., Heller, G. R., Ramirez, T. K., Choi, H., Luviano, J., et al. (2021) · Nature · doi:10.1038/s41586-020-03171-x · Cited by 637 · see citing works

Observing the unwatchable through acceleration logging of animal behavior

Brown, D. D., Kays, R., Wikelski, M. C., Wilson, R. P., & Klimley, A. P. (2013) · Animal Biotelemetry · doi:10.1186/2050-3385-1-20 · Cited by 627 · see citing works

Camera traps in animal ecology : methods and analyses

O’Connell, A. F., Nichols, J. D., & Kāranta, K. U. (2011) · Cited by 615 · see citing works

Bonsai: an event-based framework for processing and controlling data streams

Lopes, G., Bonacchi, N., Frazão, J., Neto, J. P., Atallah, B. V., Soares, S., MOREIRA, L. M. A. G., Matias, S., Itskov, P. M., Correia, P. A., et al. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00007 · Cited by 602 · see citing works

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Camomilla, V., Bergamini, E., Fantozzi, S., & Vannozzi, G. (2018) · Sensors · doi:10.3390/s18030873 · Cited by 601 · see citing works

Rigid body motion calculated from spatial co-ordinates of markers

Spoor, C. W., & Veldpaus, F. E. (1980) · Journal of Biomechanics · doi:10.1016/0021-9290(80)90020-2 · Cited by 585 · see citing works

PifPaf: Composite Fields for Human Pose Estimation

Kreiss, S., Bertoni, L., & Alahi, A. (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.2019.01225 · Cited by 574 · see citing works

3D Human Pose Estimation = 2D Pose Estimation + Matching

Chen, C.-H., & Ramanan, D. (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.2017.610 · Cited by 569 · see citing works

Mapping the stereotyped behaviour of freely moving fruit flies

Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565 · see citing works

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547 · see citing works

Using Pre-Training Can Improve Model Robustness and UncertaintyPreprint

Hendrycks, D., Lee, K., & Mazeika, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09960 · Cited by 531 · see citing works

Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology

Siegle, J. H., Cuevas-López, A., Patel, Y., Abramov, K., Ohayon, S., & Voigts, J. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5eea · Cited by 530 · see citing works

Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks

Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T., Yuan, J., & Magnenat‐Thalmann, N. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00236 · Cited by 522 · see citing works

Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

Tomè, D., Russell, C., & Agapito, L. (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.2017.603 · Cited by 505 · see citing works

Pliocene footprints in the Laetolil Beds at Laetoli, northern Tanzania

Leakey, M. D., & Hay, R. L. (1979) · Nature · doi:10.1038/278317a0 · Cited by 493 · see citing works

Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00057.x · Cited by 476 · see citing works

Contrastive Multiview Coding

Tian, Y., Krishnan, D., & Isola, P. (2019) · arXiv · doi:10.48550/arxiv.1906.05849 · Cited by 465 · see citing works

Computational Neuroethology: A Call to Action

Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.038 · Cited by 453 · see citing works

Deep learning tools for the measurement of animal behavior in neuroscience

Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450 · see citing works

Biomechanical and physiological aspects of legged locomotion in humans

Saibene, F., & Minetti, A. E. (2003) · European Journal of Applied Physiology · doi:10.1007/s00421-002-0654-9 · Cited by 440 · see citing works

A computer vision for animal ecology

Weinstein, B. G. (2017) · Journal of Animal Ecology · doi:10.1111/1365-2656.12780 · Cited by 435 · see citing works

vq-wav2vec: Self-Supervised Learning of Discrete Speech RepresentationsPreprint

Baevski, A., Schneider, S., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05453 · Cited by 431 · see citing works

An efficient framework for learning sentence representations

Logeswaran, L., & Lee, H. (2018) · arXiv · doi:10.48550/arxiv.1803.02893 · Cited by 427 · see citing works

Learning effective human pose estimation from inaccurate annotation

Johnson, S., & Everingham, M. (2011) · 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.2011.5995318 · Cited by 417 · see citing works

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020) · neural information processing systems · Cited by 386 · see citing works

3D Menagerie: Modeling the 3D Shape and Pose of Animals

Zuffi, S., Kanazawa, A., Jacobs, D. W., & Black, M. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.586 · Cited by 381 · see citing works

Untitled work

(2019) · neural information processing systems · Cited by 350 · see citing works

Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing

Sabatini, A. M. (2011) · Sensors · doi:10.3390/s110201489 · Cited by 349 · see citing works

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A. S., Bethge, M., & Brendel, W. (2019) · arXiv · doi:10.48550/arxiv.1907.07484 · Cited by 324 · see citing works

Anipose: A toolkit for robust markerless 3D pose estimation

Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324 · see citing works

Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke

Schwarz, A., Kanzler, C. M., Lambercy, O., Luft, A. R., & Veerbeek, J. M. (2019) · Stroke · doi:10.1161/strokeaha.118.023531 · Cited by 315 · see citing works

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

von Marcard, T., Rosenhahn, B., Black, M. J., & Pons‐Moll, G. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13131 · Cited by 309 · see citing works

Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry

Kocabas, M., Karagoz, S., & Akbaş, E. (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.2019.00117 · Cited by 297 · see citing works

Multi-Task Self-Supervised Learning for Robust Speech Recognition

Ravanelli, M., Zhong, J., Pascual, S., Świętojański, P., Monteiro, J., Trmal, J., & Bengio, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053569 · Cited by 276 · see citing works

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Maceira-Elvira, P., Popa, T., Schmid, A., & Hummel, F. C. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0612-y · Cited by 275 · see citing works

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

(2018) · doi:10.1109/cvprw44695.2018 · Cited by 267 · see citing works

ArtTrack: Articulated Multi-Person Tracking in the Wild

Insafutdinov, E., Andriluka, M., Pishchulin, L., Tang, S., Levinkov, E., Andres, B., & Schiele, B. (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.2017.142 · Cited by 246 · see citing works

Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions

Sturman, O., von Ziegler, L., Schläppi, C., Akyol, F., Privitera, M., Slominski, D., Grimm, C., Thieren, L., Zerbi, V., Grewe, B. F., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0776-y · Cited by 243 · see citing works

Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio

Bala, P., Eisenreich, B. R., Yoo, S. B. M., Hayden, B. Y., Park, H. S., & Zimmermann, J. (2020) · Nature Communications · doi:10.1038/s41467-020-18441-5 · Cited by 238 · see citing works

Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning

de Chaumont, F., Ey, E., Torquet, N., Lagache, T., Dallongeville, S., Imbert, A., Legou, T., Sourd, A. L., Fauré, P., Bourgeron, T., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0396-1 · Cited by 235 · see citing works

Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment

Tracey, I., Woolf, C. J., & Andrews, N. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.02.019 · Cited by 231 · see citing works

Reverse-engineering the locomotion of a stem amniote

Nyakatura, J. A., Melo, K., Horvat, T., Karakasiliotis, K., Allen, V. R., Andikfar, A., Andrada, E., Arnold, P., Lauströer, J., Hutchinson, J. R., et al. (2019) · Nature · doi:10.1038/s41586-018-0851-2 · Cited by 224 · see citing works

Identifying behavioral structure from deep variational embeddings of animal motion

Luxem, K., Mocellin, P., Fuhrmann, F., Kürsch, J., Miller, S. R., Palop, J. J., Remy, S., & Bauer, P. (2022) · Communications Biology · doi:10.1038/s42003-022-04080-7 · Cited by 218 · see citing works

Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept

Ceseracciu, E., Sawacha, Z., & Cobelli, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087640 · Cited by 210 · see citing works

Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data

Zhou, Y., Habermann, M., Xu, W., Habibie, I., Theobalt, C., & Xu, F. (2020) · 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/cvpr42600.2020.00539 · Cited by 208 · see citing works

Biomechanics of predator–prey arms race in lion, zebra, cheetah and impala

Wilson, A. M., Hubel, T. Y., Wilshin, S. D., Lowe, J. C., Lorenc, M., Dewhirst, O. P., Bartlam‐Brooks, H. L. A., Diack, R., Bennitt, E., Golabek, K. A., et al. (2018) · Nature · doi:10.1038/nature25479 · Cited by 201 · see citing works

Self-supervised Learning of Motion CapturePreprint

Tung, H.-Y. F., Tung, H.-W., Yumer, E., & Fragkiadaki, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01337 · Cited by 198 · see citing works

Real-time, low-latency closed-loop feedback using markerless posture tracking

Kane, G. A., Lopes, G., Saunders, J. L., Mathis, A., & Mathis, M. W. (2020) · eLife · doi:10.7554/elife.61909 · Cited by 194 · see citing works

Neurodata Without Borders: Creating a Common Data Format for Neurophysiology

Teeters, J. L., Godfrey, K. B., Young, R., Dang, C., Friedsam, C., Wark, B., Asari, H., Peron, S., Li, N., Peyrache, A., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.025 · Cited by 193 · see citing works

Benchmarking and Error Diagnosis in Multi-instance Pose Estimation

Ronchi, M. R., & Perona, P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.48 · Cited by 187 · see citing works

All the light that we can see: a new era in miniaturized microscopy

Aharoni, D. B., Khakh, B. S., Silva, A. J., & Golshani, P. (2018) · Nature Methods · doi:10.1038/s41592-018-0266-x · Cited by 185 · see citing works

DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Günel, S., Rhodin, H., Morales, D., Campagnolo, J., Ramdya, P., & Fua, P. (2019) · eLife · doi:10.7554/elife.48571 · Cited by 167 · see citing works

Engineering a Less Artificial Intelligence

Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.034 · Cited by 164 · see citing works

Two Distinct Types of Eye-Head Coupling in Freely Moving Mice

Meyer, A. F., O'Keefe, J. M., & Poort, J. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.042 · Cited by 162 · see citing works

Pretraining boosts out-of-domain robustness for pose estimation

Mathis, A., Biasi, T., Schneider, S., Yüksekgönül, M., Rogers, B., Bethge, M., & Mathis, M. W. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00190 · Cited by 143 · see citing works

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16808-1_21 · Cited by 142 · see citing works

Advanced Neurotechnologies for the Restoration of Motor Function

Micera, S., Caleo, M., Chisari, C., Hummel, F. C., & Pedrocchi, A. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.039 · Cited by 140 · see citing works

Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”

Zuffi, S., Kanazawa, A., Berger-Wolf, T., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00546 · Cited by 139 · cited 1× · see citing works

Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning

Bidder, O. R., Walker, J. S., Jones, M. W., Holton, M. D., Urgé, P., Scantlebury, D. M., Marks, N. J., Magowan, E. A., Maguire, I. E., & Wilson, R. P. (2015) · Movement Ecology · doi:10.1186/s40462-015-0055-4 · Cited by 138 · see citing works

Self-supervised learning of motion capture

Tung, H.-Y. F., Tung, H.-W., Yumer, E., & Fragkiadaki, K. (2017) · neural information processing systems · Cited by 123 · see citing works

MacaquePose: A Novel “In the Wild” Macaque Monkey Pose Dataset for Markerless Motion Capture

Labuguen, R. T., Matsumoto, J., Negrete, S. B., Nishimaru, H., Nishijo, H., Takada, M., Go, Y., Inoue, K., & Shibata, T. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2020.581154 · Cited by 120 · see citing works

An IMU-to-Body Alignment Method Applied to Human Gait Analysis

Vargas-Valencia, L., Elías, A., Rocón, E., Bastos-Filho, T., & Frizera, A. (2016) · Sensors · doi:10.3390/s16122090 · Cited by 116 · see citing works

EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera

Xu, L., Xu, W., Golyanik, V., Habermann, M., Fang, L., & Theobalt, C. (2020) · 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/cvpr42600.2020.00502 · Cited by 116 · see citing works

2013 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

(2012) · IEEE Pulse · doi:10.1109/mpul.2012.2210847 · Cited by 114 · see citing works

Human movement analysis: The soft tissue artefact issue

Camomilla, V., Dumas, R., & Cappozzo, A. (2017) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2017.09.001 · Cited by 108 · see citing works

DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data

Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., & Golshani, P. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00020 · Cited by 108 · see citing works

How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation

Fan, B., Li, Q., & Liu, T. (2017) · Sensors · doi:10.3390/s18010076 · Cited by 106 · see citing works

A novel biomechanical approach for animal behaviour recognition using accelerometers

Chakravarty, P., Cozzi, G., Özgül, A., & Aminian, K. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13172 · Cited by 103 · see citing works

Rethinking ImageNet Pre-trainingPreprint

He, K., Girshick, R., & Dollár, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08883 · Cited by 97 · see citing works

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

Begon, M., Andersen, M. S., & Dumas, R. (2017) · Journal of Biomechanical Engineering · doi:10.1115/1.4038741 · Cited by 95 · see citing works

Video can make behavioural science more reproducible

Gilmore, R. O., & Adolph, K. E. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0128 · Cited by 81 · see citing works

Fusing Visual and Inertial Sensors with Semantics for 3D Human Pose Estimation

Gilbert, A., Trumble, M., Malleson, C., Hilton, A., & Collomosse, J. P. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1118-y · Cited by 77 · see citing works

Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex

Berger, M., Agha, N. S., & Gail, A. (2020) · eLife · doi:10.7554/elife.51322 · Cited by 71 · see citing works

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

Lebleu, J., Gosseye, T. P., Detrembleur, C., Mahaudens, P., Cartiaux, O., & Penta, M. (2020) · Sensors · doi:10.3390/s20030715 · Cited by 69 · see citing works

Movement science needs different pose tracking algorithms

Seethapathi, N., Wang, S., Saluja, R., Blohm, G., & Kording, K. P. (2019) · arXiv · doi:10.48550/arxiv.1907.10226 · Cited by 64 · see citing works

The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research

White, S. R., Amarante, L. M., Kravitz, A. V., et al. (2019) · eNeuro · doi:10.1523/eneuro.0223-19.2019 · Cited by 59 · see citing works

A moving fluoroscope to capture tibiofemoral kinematics during complete cycles of free level and downhill walking as well as stair descent

List, R., Postolka, B., Schütz, P., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185952 · Cited by 58 · see citing works

A kinematic synergy for terrestrial locomotion shared by mammals and birds

Catavitello, G., Ivanenko, Y. P., & Lacquaniti, F. (2018) · eLife · doi:10.7554/elife.38190 · Cited by 58 · see citing works

Brain-wide representations of ongoing behavior: a universal principle?

Kaplan, H. S., & Zimmer, M. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.02.008 · Cited by 57 · see citing works

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Khan, M. H., McDonagh, J., Khan, S. H., Shahabuddin, M., Arora, A., Khan, F. S., Shao, L., & Tzimiropoulos, G. (2020) · 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/cvpr42600.2020.00697 · Cited by 54 · see citing works

Self-supervised Keypoint Correspondences for Multi-person Pose Estimation and Tracking in Videos

Rafi, U., Doering, A., Leibe, B., & Gall, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_3 · Cited by 54 · see citing works

MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence

Yao, Y., Jafarian, Y., & Park, H. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00084 · Cited by 51 · see citing works

Methodological concerns using intra‐cortical pins to measure tibiofemoral kinematics

Ramsey, D. K., Wretenberg, P. F., Benoit, D. L., et al. (2003) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-003-0388-1 · Cited by 50 · see citing works

The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges

Madhav, M. S., & Cowan, N. J. (2020) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-060117-104856 · Cited by 45 · see citing works

Arm movements induced by noninvasive optogenetic stimulation of the motor cortex in the common marmoset

Ebina, T., Obara, K., Watakabe, A., Masamizu, Y., Terada, S.-I., Matoba, R., Takaji, M., Hatanaka, N., Nambu, A., Mizukami, H., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903445116 · Cited by 43 · see citing works

Sparse Inertial PoserPreprint

von MarcardT., RosenhahnB., BlackM., J., & Pons-MollG. (2017) · arXiv (Cornell University) · Cited by 42 · see citing works

Understanding Human Motion: A Historic Review

Klette, R., & Tee, G. J. (2007) · Computational imaging and vision · doi:10.1007/978-1-4020-6693-1_1 · Cited by 40 · see citing works

Manual dexterity of mice during food-handling involves the thumb and a set of fast basic movements

Barrett, J. M., Tapies, M. G. R., & Shepherd, G. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226774 · Cited by 40 · see citing works

Wireless inertial measurement of head kinematics in freely-moving rats

Pasquet, M. O., Tihy, M., Gourgeon, A., Pompili, M. N., Godsil, B. P., Léna, C., & Dugué, G. P. (2016) · Scientific Reports · doi:10.1038/srep35689 · Cited by 38 · see citing works

Scientific Community Image Forum: A discussion forum for scientific image software

Rueden, C., Ackerman, J., Arena, E. T., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000340 · Cited by 37 · see citing works

Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN SupervisionPreprint

Mehta, D., Rhodin, H., Casas, D., et al. (2016) · arXiv (Cornell University) · Cited by 34 · see citing works

On the inference speed and video-compression robustness of DeepLabCutPreprint

Mathis, A., & Warren, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/457242 · Cited by 34 · see citing works

FreiPose: A Deep Learning Framework for Precise Animal Motion Capture in 3D SpacesPreprint

Zimmermann, C., Schneider, A., Alyahyay, M., Brox, T., & Diester, I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.27.967620 · Cited by 31 · see citing works

An Analysis of Pre-Training on Object DetectionPreprint

Li, H., Singh, B., Najibi, M., Wu, Z., & Davis, L. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05871 · Cited by 30 · see citing works

Mechanical principles of dynamic terrestrial self-righting using wings

Li, C., Kessens, C. C., Fearing, R. S., et al. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1372213 · Cited by 28 · see citing works

A Matlab-based toolbox for characterizing behavior of rodents engaged in string-pulling

Inayat, S., Singh, S., Ghasroddashti, A., et al. (2020) · eLife · doi:10.7554/elife.54540 · Cited by 28 · see citing works

Integrating XMALab and DeepLabCut for high-throughput XROMM

Laurence-Chasen, J. D., Manafzadeh, A. R., Hatsopoulos, N. G., Ross, C. F., & Arce‐McShane, F. I. (2020) · Journal of Experimental Biology · doi:10.1242/jeb.226720 · Cited by 28 · see citing works

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Sandbrink, K. J., Mamidanna, P., Michaelis, C., Bethge, M., Mathis, M. W., & Mathis, A. (2023) · eLife · doi:10.7554/elife.81499 · Cited by 27 · see citing works

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose EstimationPreprint

Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7963 · Cited by 26 · see citing works

Simultaneous Identification and Tracking of Multiple People Using Video and IMUs

Henschel, R. D., von Marcard, T., & Rosenhahn, B. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00106 · Cited by 26 · see citing works

OptiFlex: video-based animal pose estimation using deep learning enhanced by optical flowPreprint

Liu, X., Yu, S., Flierman, N. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.04.025494 · Cited by 25 · see citing works

Biomechanics in posture space: Properties and relevance of principal accelerations for characterizing movement control

Longo, A., Haid, T. H., Meulenbroek, R. G. J., et al. (2018) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2018.11.031 · Cited by 24 · see citing works

Monocular 3D Human Pose Estimation In The Wild Using Improved CNN SupervisionPreprint

Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., & Theobalt, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09813 · Cited by 22 · see citing works

Behavioral and Neural Variability of Naturalistic Arm Movements

Peterson, S. M., Singh, S. H., Wang, N. X. R., Rao, R. P. N., & Brunton, B. W. (2021) · eNeuro · doi:10.1523/eneuro.0007-21.2021 · Cited by 20 · see citing works

ArtTrack: Articulated Multi-person Tracking in the WildPreprint

Insafutdinov, E., Andriluka, M., Pishchulin, L., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01465 · Cited by 17 · see citing works

Knee joint kinematics and kinetics during the hop and cut after soft tissue artifact suppression: Time to reconsider ACL injury mechanisms?

Smale, K. B., Potvin, B. M., Shourijeh, M. S., et al. (2017) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2017.06.049 · Cited by 15 · see citing works

Transferring Dense Pose to Proximal Animal Classes

Sanakoyeu, A., Khalidov, V., McCarthy, M. S., Vedaldi, A., & Neverova, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00528 · Cited by 13 · see citing works

FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition

Huang, Y., Sun, B., Kan, H., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31726-3_10 · Cited by 12 · see citing works

Data architecture for a large-scale neuroscience collaborationPreprint

Bonacchi, N., Chapuis, G., Churchland, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/827873 · Cited by 6 · see citing works

Perspectives in machine learning for wildlife conservation

Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works

Anipose: A toolkit for robust markerless 3D pose estimation

Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324 · see citing works

Perspectives on Individual Animal Identification from Biology and Computer Vision

Vidal, M., Wolf, N., Rosenberg, B., Harris, B. P., & Mathis, A. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab107 · Cited by 104 · see citing works

Neural circuits regulating prosocial behaviors

Walsh, J. J., Christoffel, D. J., & Malenka, R. C. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-022-01348-8 · Cited by 72 · see citing works

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

Joska, D., Clark, L., Muramatsu, N., Jericevich, R., Nicolls, F., Mathis, A., Mathis, M. W., & Patel, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561338 · Cited by 66 · see citing works

Preliminary Discussion on Several Primer Sealers

Shao-y, Q. (2005) · Paint & coatings industry

To design PCR primers with Oligo 6 and Primer Premier 5

Gao, Y. (2004) · Cited by 20 · see citing works

Chapter Two: Primer? Primer? We Don’t Need a Stinking Primer!

Swensson, J., Ellis, J. R., & Shaffer, M. (2019) · Rowman & Littlefield Publishers eBooks · doi:10.5771/9781475850772-9

A Primer on PlayA Primer on Play

Elkind, D. (2014) · Contemporary Psychology · doi:10.1037/a0036867

Study of Anti-corrosion Performance of Primer in Water Environment

Yan-yan, Y. (2008) · Equipment Environmental Engineering