Deep learning for visual understanding: A review
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The prior deep learning surveys discussed only one modality as the data source for data-driven learning.
“Nonetheless, the applications discussed in these surveys include only a single modality as a data source for data-driven learning.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [179]
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 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · 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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
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
(2014) · Cited by 33,419 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379 · see citing works
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Multi-column deep neural networks for image classification
Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · 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.2012.6248110 · Cited by 3,760 · see citing works
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642 · see citing works
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,401 · 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
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · see citing works
Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · 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.2009.5206757 · Cited by 2,895 · see citing works
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656 · see citing works
Improving the Fisher Kernel for Large-Scale Image Classification
Perronnin, F., Sánchez, J., & Mensink, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_11 · Cited by 2,588 · see citing works
Donoho, D. L. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20132 · Cited by 2,474 · see citing works
Efficient sparse coding algorithms
Lee, H., Battle, A. J., Raina, R., & Ng, A. Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0105 · Cited by 2,449 · see citing works
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · see citing works
Online Learning for Matrix Factorization and Sparse CodingPreprint
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · arXiv (Cornell University) · doi:10.1145/1756006.1756008 · Cited by 2,305 · see citing works
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
Masci, J., Meier, U., Ciresan, D., & Schmidhuber, J. (2011) · International Conference on Artificial Neural Networks · doi:10.1007/978-3-642-21735-7_7 · Cited by 2,185 · see citing works
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,180 · see citing works
Pictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,167 · see citing works
Online dictionary learning for sparse coding
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553463 · Cited by 2,147 · see citing works
(2013) · Cited by 2,056 · see citing works
Pathwise coordinate optimization
Friedman, J. H., Hastie, T. J., Höfling, H., & Tibshirani, R. J. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas131 · Cited by 1,906 · see citing works
Zeiler, M. D., Krishnan, D., Taylor, G. W., & Fergus, R. (2010) · 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.2010.5539957 · Cited by 1,753 · see citing works
An HOG-LBP human detector with partial occlusion handling
Wang, X., Han, T. X., & Yan, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459207 · Cited by 1,727 · see citing works
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695 · see citing works
Convolutional neural networks at constrained time cost
He, K., & Sun, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299173 · Cited by 1,558 · see citing works
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,556 · see citing works
Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,514 · see citing works
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works
Learning Fast Approximations of Sparse Coding
Gregor, K., & LeCun, Y. (2010) · Cited by 1,417 · see citing works
Deep Convolutional Network Cascade for Facial Point Detection
Sun, Y. J., Wang, X., & Tang, X. (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.2013.446 · Cited by 1,405 · see citing works
Efficient object localization using Convolutional Networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298664 · Cited by 1,365 · 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
Deep Neural Networks for Object Detection
Szegedy, C., Toshev, A., & Erhan, D. (2013) · Cited by 1,313 · see citing works
Exploiting Generative Models in Discriminative Classifiers
Jaakkola, T., & Haussler, D. H. (1998) · Cited by 1,274 · see citing works
Adaptive deconvolutional networks for mid and high level feature learning
Zeiler, M. D., Taylor, G. W., & Fergus, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126474 · Cited by 1,265 · see citing works
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229 · see citing works
Neural Codes for Image Retrieval
Babenko, A., Slesarev, A., Chigorin, A., & Lempitsky, V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_38 · Cited by 1,225 · see citing works
A Theoretical Analysis of Feature Pooling in Visual Recognition
Boureau, Y.-L., Ponce, J., & LeCun, Y. (2010) · Cited by 1,196 · see citing works
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011) · Cited by 1,189 · see citing works
Measuring the Objectness of Image Windows
Alexe, B., Deselaers, T., & Ferrari, V. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.28 · Cited by 1,182 · see citing works
Simultaneous Detection and Segmentation
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,176 · see citing works
Scalable Object Detection Using Deep Neural Networks
Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.276 · Cited by 1,161 · cited 2× · see citing works
“Derived Models AlexNet[6] Clarifai[52] SPP-net[26] VGG[31] GoogLeNet[20] Image Classification RCNN[29] FCN[147] Object Detection Semantic Segmentation 2.1.3.3.”
MethodInfluentialDeep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Dai, J., He, K., & Sun, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.191 · Cited by 1,118 · see citing works
Efficient Learning of Sparse Representations with an Energy-Based Model
Ranzato, M., Poultney, C., Chopra, S., & LeCun, Y. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0147 · Cited by 1,106 · see citing works
Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057 · see citing works
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Gong, Y., Wang, L., Guo, R., & Lazebnik, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_26 · Cited by 1,043 · see citing works
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Cheng, M., Zhang, Z., Lin, W.-Y., & Torr, P. H. S. (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.2014.414 · Cited by 1,042 · see citing works
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.203 · Cited by 1,041 · see citing works
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., & Yu, Y. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.314 · Cited by 936 · see citing works
Is object localization for free? - Weakly-supervised learning with convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298668 · Cited by 886 · see citing works
On optimization methods for deep learning
Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q. V., & Ng, A. Y. (2011) · International Conference on Machine Learning · Cited by 868 · see citing works
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862 · see citing works
(2007) · Cited by 849 · see citing works
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Johnson, S., & Everingham, M. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.12 · Cited by 826 · see citing works
Cadieu, C. F., Hong, H., Yamins, D. L. K., Pinto, N., Ardila, D., Solomon, E. A., Majaj, N. J., & DiCarlo, J. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003963 · Cited by 824 · see citing works
Deep Learning for Content-Based Image Retrieval
Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., & Li, J. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654948 · Cited by 814 · see citing works
Parallel optimization: Theory, algorithms, and applications
(1998) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(98)90713-1 · Cited by 813 · see citing works
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801 · see citing works
Stochastic Pooling for Regularization of Deep Convolutional Neural NetworksPreprint
Zeiler, M. D., & Fergus, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3557 · Cited by 782 · see citing works
A tutorial survey of architectures, algorithms, and applications for deep learning
Deng, L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2013.9 · Cited by 737 · see citing works
Chambolle, A., Vore, R. A. D., Lee, N.-Y., & Lucier, B. J. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661182 · Cited by 733 · see citing works
Nonlinear Learning using Local Coordinate Coding
Yu, K., Zhang, T., & Gong, Y. (2009) · Cited by 681 · see citing works
On Contrastive Divergence Learning.
Carreira-Perpiñán, M. Á., & Hinton, G. E. (2005) · Cited by 630 · see citing works
Deep Learning via Semi-supervised Embedding
Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 577 · see citing works
Flowing ConvNets for Human Pose Estimation in Videos
Pfister, T., Charles, J., & Zisserman, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.222 · Cited by 530 · see citing works
Diagnosing Error in Object Detectors
Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_25 · Cited by 520 · see citing works
(2011) · Cited by 514 · see citing works
Image Classification Using Super-Vector Coding of Local Image Descriptors
Zhou, X., Yu, K., Zhang, T., & Huang, T. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_11 · Cited by 503 · see citing works
Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment
Zhang, J., Shan, S., Kan, M., & Chen, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_1 · Cited by 479 · see citing works
An Efficient Learning Procedure for Deep Boltzmann Machines
Salakhutdinov, R., & Hinton, G. E. (2012) · Neural Computation · doi:10.1162/neco_a_00311 · Cited by 477 · see citing works
Liou, C., Cheng, W., Liou, J.-W., & Liou, D.-R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.055 · Cited by 473 · see citing works
Feedforward semantic segmentation with zoom-out features
Mostajabi, M., Yadollahpour, P., & Shakhnarovich, G. (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.2015.7298959 · Cited by 446 · see citing works
DeepID-Net: Deformable deep convolutional neural networks for object detection
Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Li, H., Yang, S., Wang, Z., Loy, C. C., et al. (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.2015.7298854 · Cited by 442 · see citing works
Visualizing and Understanding Convolutional Neural NetworksPreprint
Zeiler, M. D., & Fergus, R. (2013) · arXiv (Cornell University) · Cited by 440 · see citing works
Category Independent Object Proposals
Endres, I., & Hoiem, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_42 · Cited by 433 · see citing works
MODEC: Multimodal Decomposable Models for Human Pose Estimation
Sapp, B., & Taskar, B. (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.2013.471 · Cited by 433 · see citing works
Analyzing the Performance of Multilayer Neural Networks for Object Recognition
Agrawal, P., Girshick, R., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_22 · Cited by 427 · see citing works
Convolutional feature masking for joint object and stuff segmentation
Dai, J., He, K., & Sun, J. (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.2015.7299025 · Cited by 425 · see citing works
Local features are not lonely – Laplacian sparse coding for image classification
Gao, S., Tsang, I. W.-H., Chia, L.-T., & Zhao, P. (2010) · 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.2010.5539943 · Cited by 420 · see citing works
Higher Order Contractive Auto-Encoder
Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., & Glorot, X. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_41 · Cited by 410 · see citing works
NEIL: Extracting Visual Knowledge from Web Data
Chen, X., Shrivastava, A., & Gupta, A. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.178 · Cited by 408 · see citing works
Learning hierarchical representations for face verification with convolutional deep belief networks
Huang, G. B., Lee, H., & Miller, E. G. (2012) · 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.2012.6247968 · Cited by 408 · see citing works
Large-scale image classification: Fast feature extraction and SVM training
Lin, Y., Lv, F., Shenghuo Zhu, B., Yan, S., Cour, T., Yu, K., Cao, L., & Huang, T. S. (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.2011.5995477 · Cited by 388 · see citing works
Unsupervised learning of hierarchical representations with convolutional deep belief networks
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2011) · Communications of the ACM · doi:10.1145/2001269.2001295 · Cited by 386 · see citing works
Poselet Conditioned Pictorial Structures
Pishchulin, L., Andriluka, M., Gehler, P., & Schiele, B. (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.2013.82 · Cited by 375 · 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
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
Gao, S., Tsang, I. W.-H., & Chia, L.-T. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.63 · Cited by 356 · see citing works
Efficient Learning of Deep Boltzmann Machines
Salakhutdinov, R., & Larochelle, H. (2010) · Cited by 352 · see citing works
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise RelationsPreprint
Chen, X., & Yuille, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.3399 · Cited by 352 · see citing works
Deep Image: Scaling up Image RecognitionPreprint
Wu, R., Yan, S., Shan, Y., Dang, Q., & Sun, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.02876 · Cited by 345 · see citing works
Measuring Invariances in Deep Networks
Goodfellow, I., Lee, H., Le, Q. V., Saxe, A., & Ng, A. Y. (2009) · Cited by 337 · see citing works
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2012) · Cited by 336 · see citing works
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image SegmentationPreprint
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02734 · Cited by 323 · see citing works
3D Object Recognition with Deep Belief Nets
Nair, V. B., & Hinton, G. E. (2009) · Cited by 299 · see citing works
Tiled convolutional neural networks
Ngiam, J., Chen, Z., Chia, D., Koh, P. W., Le, Q. V., & Ng, A. Y. (2010) · neural information processing systems · Cited by 295 · see citing works
Human Pose Estimation Using Body Parts Dependent Joint Regressors
Dantone, M., Gall, J., Leistner, C., & Gool, L. 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.2013.391 · Cited by 293 · see citing works
Adaptive dropout for training deep neural networks
Ba, J. L., & Frey, B. J. (2013) · neural information processing systems · Cited by 290 · see citing works
LSDA: Large Scale Detection Through Adaptation
Hoffman, J., Guadarrama, S., Tzeng, E., Hu, R., Donahue, J., Girshick, R., Darrell, T., & Saenko, K. (2014) · arXiv · doi:10.48550/arxiv.1407.5035 · Cited by 284 · see citing works
Multi-source Deep Learning for Human Pose Estimation
Ouyang, W., Chu, X., & Wang, X. (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.2014.299 · Cited by 269 · see citing works
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine
Dahl, G. E., Ranzato, M., Mohamed, A., & Hinton, G. E. (2010) · Cited by 268 · see citing works
Learning Everything about Anything: Webly-Supervised Visual Concept Learning
Divvala, S. K., Farhadi, A., & Guestrin, C. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.412 · Cited by 267 · see citing works
Dropout Training as Adaptive RegularizationPreprint
Wager, S., Wang, S., & Liang, P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.1493 · Cited by 260 · see citing works
A fast Iterative Shrinkage-Thresholding Algorithm with application to wavelet-based image deblurring
Beck, A., & Teboulle, M. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959678 · Cited by 244 · see citing works
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
Eichner, M. L., Marín-Jiménez, M. J., Zisserman, A., & Ferrari, V. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0524-9 · Cited by 232 · see citing works
Zhang, Y., Sohn, K., Villegas, R., Pan, G., & Lee, H. (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.2015.7298621 · Cited by 219 · see citing works
How good are detection proposals, really?
Hosang, J., Benenson, R., & Schiele, B. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.24 · Cited by 215 · see citing works
Learning image representations from the pixel level via hierarchical sparse coding
Yu, K., Lin, Y., & Lafferty, J. (2011) · doi:10.1109/cvpr.2011.5995732 · Cited by 212 · see citing works
Transfer learning for Latin and Chinese characters with Deep Neural Networks
Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252544 · Cited by 210 · see citing works
Fast Inference in Sparse Coding Algorithms with Applications to Object RecognitionPreprint
Kavukcuoglu, K., Ranzato, M., & LeCun, Y. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.3467 · Cited by 199 · see citing works
Contextualizing object detection and classification
Zheng, S., Chen, Q. K., Huang, Z., Yang, H., & Yan, S. (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.2011.5995330 · Cited by 187 · see citing works
Learning Human Pose Estimation Features with Convolutional NetworksPreprint
Jain, A., Tompson, J., Andriluka, M., Taylor, G. W., & Bregler, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.7302 · Cited by 180 · see citing works
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
Kae, A., Sohn, K., Lee, H., & Miller, E. G. (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.2013.263 · Cited by 176 · see citing works
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach
Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I., Lavoie, E., Muller, X., Desjardins, G., Warde-Farley, D., et al. (2011) · International Conference on Machine Learning · Cited by 176 · see citing works
Beyond Physical Connections: Tree Models in Human Pose Estimation
Wang, F., & Li, Y. (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.2013.83 · Cited by 153 · see citing works
Multi-stage Contextual Deep Learning for Pedestrian Detection
Zeng, X., Ouyang, W., & Wang, X. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.22 · Cited by 150 · see citing works
Deep Fisher Networks for Large-Scale Image Classification
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · neural information processing systems · Cited by 149 · see citing works
On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates
Younès, L. (1999) · Stochastics and stochastics reports · doi:10.1080/17442509908834179 · Cited by 146 · see citing works
Learning Invariant Feature Hierarchies
LeCun, Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33863-2_51 · Cited by 145 · 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
Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation
Tian, Y., Zitnick, C. L., & Narasimhan, S. G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_19 · Cited by 141 · see citing works
Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos
Pfister, T., Simonyan, K., Charles, J., & Zisserman, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_35 · Cited by 140 · see citing works
Ngiam, J., Chen, Z., Koh, P. W., & Ng, A. Y. (2011) · Cited by 137 · see citing works
Distributed optimization of deeply nested systemsPreprint
Carreira-Perpiñán, M. Á., & Wang, W. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.5921 · Cited by 134 · see citing works
segDeepM: Exploiting segmentation and context in deep neural networks for object detection
Zhu, Y., Urtasun, R., Salakhutdinov, R., & Fidler, S. (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.2015.7299102 · Cited by 131 · see citing works
Deep Learning of Representations: Looking ForwardPreprint
Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.0445 · Cited by 128 · see citing works
Multi-Prediction Deep Boltzmann Machines
Goodfellow, I., Mirza, M., Courville, A. C., & Bengio, Y. (2013) · neural information processing systems · Cited by 120 · see citing works
A Better Way to Pretrain Deep Boltzmann Machines
Hinton, G. E., & Salakhutdinov, R. (2012) · neural information processing systems · Cited by 112 · see citing works
Weakly-supervised Discovery of Visual Pattern ConfigurationsPreprint
Song, H. O., Lee, Y. J., Jegelka, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6507 · Cited by 110 · see citing works
What Regularized Auto-Encoders Learn from the Data Generating DistributionPreprint
Alain, G., & Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.4246 · Cited by 104 · see citing works
Multi-scale pyramid pooling for deep convolutional representation
Yoo, D., Park, S., Lee, J., & Kweon, I. S. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301274 · Cited by 98 · see citing works
(2010) · Cited by 94 · see citing works
Statistical Modeling of Photographic Images
Simoncelli, E. P. (2005) · Handbook of Image and Video Processing · doi:10.1016/b978-012119792-6/50089-9 · Cited by 93 · see citing works
3D object retrieval with stacked local convolutional autoencoder
Leng, B., Guo, S., Zhang, X., et al. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.09.005 · Cited by 93 · see citing works
Fan, X., Zheng, K., Lin, Y., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07159 · Cited by 90 · see citing works
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection
Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., & Yan, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.120 · Cited by 88 · see citing works
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks
Ren, J., & Li, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9488 · Cited by 82 · see citing works
Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification
Baccouche, M., Mamalet, F., Wolf, C., García, C., & Baskurt, A. M. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.124 · Cited by 81 · see citing works
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines
Cho, K., Raiko, T., & Ihler, A. (2011) · Cited by 78 · see citing works
An empirical analysis of dropout in piecewise linear networksPreprint
Warde-Farley, D., Goodfellow, I., Courville, A. C., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6197 · Cited by 72 · see citing works
Towards Unified Object Detection and Semantic Segmentation
Dong, J., Chen, Q., Yan, S., & Yuille, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_20 · Cited by 70 · see citing works
Whitteridge, D. (1959) · The Lancet · doi:10.1016/s0140-6736(59)90152-7 · Cited by 69 · see citing works
Deep Boltzmann Machines and the Centering Trick
Montavon, G., & Müller, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_33 · Cited by 68 · see citing works
Li, H., Zhao, R., & Wang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4526 · Cited by 66 · see citing works
A PAC-Bayesian Tutorial with A Dropout BoundPreprint
McAllester, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.2118 · Cited by 66 · see citing works
Zero-bias autoencoders and the benefits of co-adapting featuresPreprint
Konda, K., Memisevic, R., & Krueger, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.3337 · Cited by 65 · see citing works
Unsupervised Feature Learning by Deep Sparse Coding
He, Y., Kavukcuoglu, K., Wang, Y. H., et al. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.103 · Cited by 62 · see citing works
The Shape Boltzmann Machine: A strong model of object shape
Eslami, S. M. A., Heess, N., & Winn, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247702 · Cited by 61 · see citing works
Visualizing and Comparing Convolutional Neural NetworksPreprint
Yu, W., Yang, K., Bai, Y., Yao, H., & Rui, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6631 · Cited by 49 · see citing works
Learning to localize detected objects
Dai, Q., & Hoiem, D. (2012) · 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.2012.6248070 · Cited by 42 · see citing works
Unsupervised feature learning by augmenting single imagesPreprint
Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5242 · Cited by 40 · see citing works
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual RecognitionPreprint
Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Wei, D., & Yu, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0736 · Cited by 40 · see citing works
Deep Joint Task Learning for Generic Object ExtractionPreprint
Wang, X., Zhang, L., Lin, L., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.00743 · Cited by 39 · see citing works
ConceptLearner: Discovering visual concepts from weakly labeled image collections
Zhou, B., Jagadeesh, V., & Piramuthu, R. (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.2015.7298756 · Cited by 37 · see citing works
Analysis of Feature Maps Selection in Supervised Learning Using Convolutional Neural Networks
Chu, J. L., & Krzyżak, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06483-3_6 · Cited by 36 · see citing works
DeepIndex for Accurate and Efficient Image Retrieval
Liu, Y., Guo, Y., Wu, S., et al. (2015) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/2671188.2749300 · Cited by 35 · see citing works
Self-citationA novel sparse auto-encoder for deep unsupervised learning
Jiang, X., Zhang, Y., Zhang, W., & Xiao, X. (2013) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2013.6748512 · Cited by 35 · see citing works
Expected energy-based restricted Boltzmann machine for classification
Elfwing, S., Uchibe, E., & Doya, K. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.09.006 · Cited by 33 · see citing works
Human pose recovery by supervised spectral embedding
Yu, J., Guo, Y., Tao, D., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.04.005 · Cited by 28 · see citing works
Self-citationRobust Human Body Shape and Pose Tracking
Huang, C.-H. P., Boyer, E., & Ilić, S. (2013) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2013.45 · Cited by 27 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117 · see citing works
Albumentations: fast and flexible image augmentations
Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020) · Information · doi:10.3390/info11020125 · Cited by 2,714 · see citing works
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000 · see citing works
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021) · Electronics · doi:10.3390/electronics10202470 · Cited by 890 · see citing works
A survey on deep learning for multimodal data fusion
Gao, J., Li, P., Chen, Z., & Zhang, J. (2020) · Neural Computation · doi:10.1162/neco_a_01273 · Cited by 821 · see citing works
Abdeljaber, O., Avci, O., Kiranyaz, M. S., Boashash, B., Sodano, H., & Inman, D. J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.09.069 · Cited by 521 · see citing works
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494 · see citing works
Opening the black box: interpretable machine learning for geneticists
Azodi, C. B., Tang, J., & Shiu, S.-H. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.03.005 · Cited by 473 · see citing works
Intelligent video surveillance: A review through deep learning techniques for crowd analysis
Sreenu, G., & Saleem Durai, M. A. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0212-5 · Cited by 460 · see citing works
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
Federated Learning for intrusion detection system: Concepts, challenges and future directions
Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457 · see citing works
Computer vision for behaviour-based safety in construction: A review and future directions
Fang, W., Love, P. E. D., Luo, H., & Ding, L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100980 · Cited by 346 · see citing works
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works
Generative Adversarial Networks and Its Applications in Biomedical Informatics
Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y., & Zhou, X. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00164 · Cited by 237 · see citing works
khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
Deep Learning in Protein Structural Modeling and Design
Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227 · see citing works
Video surveillance systems-current status and future trends
Tsakanikas, V., & Dagiuklas, T. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.11.011 · Cited by 191 · see citing works
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188 · see citing works
Review on self-supervised image recognition using deep neural networks
Ohri, K., & Kumar, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107090 · Cited by 179 · see citing works
Ding, P., Zhang, Y., Deng, W.-J., Jia, P., & Kuijper, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.05.005 · Cited by 174 · see citing works
Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K. (2021) · Sensors · doi:10.3390/s21238142 · Cited by 169 · see citing works
A bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works
A comprehensive review of recent advances on deep vision systems
Abbas, Q., Ibrahim, M. E. A., & Jaffar, M. A. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9633-3 · Cited by 99 · see citing works
Hashemzadeh, M., & Zademehdi, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.019 · Cited by 100 · see citing works
Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
Pepper, A., Tischler, N., & Pryde, G. J. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.060501 · Cited by 97 · see citing works
Afandizadeh, S., Abdolahi, S., & Mirzahossein, H. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/9981657 · Cited by 64 · see citing works
Visual-based obstacle detection and tracking, and conflict detection for small UAS sense and avoid
Opromolla, R., & Fasano, G. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.107167 · Cited by 54 · see citing works
[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images
Sreelakshmy, R., Titus, A., Sasirekha, N., Logashanmugam, E., Begam, R. B., Ramkumar, G., & Raju, R. (2022) · BioMed Research International · doi:10.1155/2022/8342767 · Cited by 51 · see citing works
RetractedResidual error based knowledge distillation
Gao, M., Wang, Y., & Wan, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.113 · Cited by 45 · see citing works
Gao, J., Yang, J., Wang, G., & Li, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.055 · Cited by 44 · see citing works
Passenger flow counting in buses based on deep learning using surveillance video
Hsu, Y.-W., Wang, T.-Y., & Perng, J.-W. (2020) · Optik · doi:10.1016/j.ijleo.2019.163675 · Cited by 43 · see citing works
Borra, D., Fantozzi, S., & Magosso, E. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.655840 · Cited by 36 · see citing works
“In recent years, deep learning, a branch of ML originally proposed in computer vision (Guo et al., 2016; Ismail Fawaz et al., 2019), has been applied to decoding problems of physiological signals, such as electroencephalography, electromyography, electrocardiography, and electrooculography (Faust…”
MethodDiniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., & Paiva, A. C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.005 · Cited by 34 · see citing works
Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., Binjumah, W. M., Altuwaijri, E. A., & Nasser, M. (2023) · Processes · doi:10.3390/pr11051488 · Cited by 21 · see citing works
Siraj, M. S., & Ahad, M. A. R. (2020) · 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) · doi:10.1109/icievicivpr48672.2020.9306630 · Cited by 15 · see citing works
- Authors
- Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S.
- Affiliations
- Leiden University (NL)
- National University of Defense Technology (CN)
- Corresponding author
- Lew, Michael S.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 187
- Pages
- 27-48
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2176950688
- Semantic Scholar
- 54378aef6ec3423ad44d61de61b301be9f5c686d
- Cited by
- 2,504
- References
- 210
- Citations by year
- 2026: 111 · 2025: 236 · 2024: 284 · 2023: 305 · 2022: 335 · 2021: 350 · 2020: 343 · 2019: 239 · 2018: 182 · 2017: 90 · 2016: 27 · 2012: 1
- FWCI
- 86.55
- Type
- journal article
- Language
- en
- Open access
- Locations
- Neurocomputing (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Leiden University (2006002026)
- National University of Defense Technology (61571453)
- NWO (642.066.603)
- NVIDIA Corporation (NV72915)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- deep learning, computer vision, visual understanding, image classification, object detection, image retrieval, semantic segmentation, human pose estimation, distributed representations, deep neural network training
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