Squeeze-and-Excitation Networks

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

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · 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

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · 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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · 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

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

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

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

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

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853 · see citing works

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · 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

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works

Untitled work

(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works

Aggregated Residual Transformations for Deep Neural Networks

Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465 · see citing works

A model of saliency-based visual attention for rapid scene analysis

Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289 · see citing works

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · see citing works

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · see citing works

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works

Untitled work

(2012) · Cited by 8,009 · see citing works

Wide Residual Networks

Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works

Learning Transferable Architectures for Scalable Image Recognition

Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (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.2018.00907 · Cited by 6,631 · 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

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

Neural Architecture Search with Reinforcement LearningPreprint

Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,091 · see citing works

Computational modelling of visual attention

Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works

Residual Attention Network for Image Classification

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works

Places: A 10 Million Image Database for Scene Recognition

Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042 · see citing works

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00293 · Cited by 3,416 · 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

Recurrent Models of Visual AttentionPreprint

Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6247 · Cited by 3,286 · see citing works

Evolving Neural Networks through Augmenting Topologies

Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269 · see citing works

DARTS: Differentiable architecture search

Liu, H., Simonyan, K., & Yang, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.09055 · Cited by 3,248 · 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

Improved regularization of convolutional neural networks with cutout

DeVries, T., & Taylor, G. W. (2017) · arXiv · doi:10.48550/arxiv.1708.04552 · Cited by 2,776 · see citing works

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

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

Deep Networks with Stochastic Depth

Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_39 · Cited by 2,104 · see citing works

SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (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.2017.667 · Cited by 2,073 · see citing works

Progressive Neural Architecture Search

Liu, C., Zoph, B., Neumann, M., Shlens, J., Wei, H., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., & Murphy, K. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_2 · Cited by 1,946 · see citing works

Wide Residual NetworksPreprint

Zagoruyko, S., & Komodakis, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07146 · Cited by 1,850 · see citing works

Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498 · see citing works

Untitled work

(2018) · International Conference on Machine Learning · Cited by 1,465 · see citing works

An Empirical Exploration of Recurrent Network Architectures

Józefowicz, R., Zaremba, W., & Sutskever, I. (2015) · Cited by 1,459 · see citing works

Training Very Deep NetworksPreprint

Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,453 · see citing works

Large-Scale Evolution of Image ClassifiersPreprint

Real, E., Moore, S., Selle, A. P., Saxena, S., Suematsu, Y. I. L., Tan, J., Le, Q. V., & Kurakin, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01041 · Cited by 1,372 · see citing works

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Bell, S., Zitnick, C. L., Bala, K., & Girshick, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.314 · Cited by 1,344 · 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

Speeding up Convolutional Neural Networks with Low Rank Expansions

Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.88 · Cited by 1,164 · see citing works

AutoAugment: Learning Augmentation Policies from Data

Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2018) · arXiv · doi:10.48550/arxiv.1805.09501 · Cited by 1,106 · see citing works

Untitled work

(2014) · arXiv (Cornell University) · Cited by 942 · see citing works

Designing Neural Network Architectures using Reinforcement LearningPreprint

Baker, B., Gupta, O., Naik, N., & Raskar, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02167 · Cited by 938 · see citing works

A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information

Olshausen, B., Anderson, C., & Van Essen, D. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-11-04700.1993 · Cited by 908 · see citing works

Genetic CNN

Xie, L., & Yuille, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.154 · Cited by 904 · see citing works

Designing neural networks using genetic algorithms

Miller, G. F., Todd, P. M., & Hegde, S. S. (1989) · international conference on Genetic algorithms · Cited by 734 · see citing works

Deep Pyramidal Residual Networks

Han, D., Kim, J., & Kim, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.668 · Cited by 715 · see citing works

Dual Path NetworksPreprint

Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., & Feng, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01629 · Cited by 714 · see citing works

Lip reading sentences in the wild

Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.367 · Cited by 702 · see citing works

Gather-Excite: Exploiting Feature Context in Convolutional Neural NetworksPreprint

Hu, J., Shen, L., Albanie, S., Sun, G., & Vedaldi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12348 · Cited by 574 · see citing works

SMASH: One-Shot Model Architecture Search through HyperNetworksPreprint

Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05344 · Cited by 500 · 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/cvprw.2009.5206757 · Cited by 496 · see citing works

Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

Shen, L., Lin, Z., & Huang, Q. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_29 · Cited by 456 · see citing works

Learning to combine foveal glimpses with a third-order Boltzmann machine

Larochelle, H., & Hinton, G. E. (2010) · Cited by 433 · see citing works

Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

Cao, C., Liu, X., Yan, S., Yu, Y., Wang, J., Wang, Z., Huang, Y., Wang, L., Huang, C., Xu, W., et al. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.338 · Cited by 409 · see citing works

Genetic CNN

Xie, L., & Yuille, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · Cited by 377 · see citing works

Shake-Shake regularizationPreprint

Gastaldi, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07485 · Cited by 353 · see citing works

Regularized Evolution for Image Classifier Architecture Search

Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014780 · Cited by 328 · see citing works

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Ioannou, Y., Robertson, D., Cipolla, R., & Criminisi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.633 · Cited by 321 · see citing works

Learnable pooling with Context Gating for video classificationPreprint

Miech, A., Laptev, I., & Šivic, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06905 · Cited by 295 · see citing works

On the importance of single directions for generalizationPreprint

Morcos, A. S., Barrett, D. G. T., Rabinowitz, N. C., & Botvinick, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06959 · Cited by 269 · see citing works

PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

Zhang, X., Li, Z., Loy, C. C., & Lin, 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.2017.415 · Cited by 258 · see citing works

SMASH: One-Shot Model Architecture Search through HyperNetworks

Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2018) · International Conference on Learning Representations · Cited by 217 · see citing works

Convolutional Neural FabricsPreprint

Saxena, S., & Verbeek, J. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02492 · Cited by 171 · see citing works

DeepArchitect: Automatically Designing and Training Deep ArchitecturesPreprint

Negrinho, R. M. P., & Gordon, G. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.08792 · Cited by 128 · see citing works

Evolving Memory Cell Structures for Sequence Learning

Bayer, J., Wierstra, D., Togelius, J., & Schmidhuber, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04277-5_76 · Cited by 103 · see citing works

Multi-Level Discriminative Dictionary Learning With Application to Large Scale Image Classification

Shen, L., Sun, G., Huang, Q., Wang, S., Lin, Z., & Wu, E. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2438548 · Cited by 61 · see citing works

YOLOv4: Optimal Speed and Accuracy of Object Detection

Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Searching for MobileNetV3

Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708 · see citing works

Path Aggregation Network for Instance Segmentation

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011 · see citing works

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · see citing works

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00293 · Cited by 3,416 · see citing works

CosFace: Large Margin Cosine Loss for Deep Face Recognition

Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034 · see citing works

VGGFace2: A Dataset for Recognising Faces across Pose and Age

Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works

Attention mechanisms in computer vision: A survey

Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · 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

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415 · see citing works

Object detection in optical remote sensing images: A survey and a new benchmark

Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317 · see citing works

Second-Order Attention Network for Single Image Super-Resolution

Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010 · see citing works

Bag of Tricks for Image Classification with Convolutional Neural Networks

He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00065 · Cited by 1,680 · see citing works

Hybrid Task Cascade for Instance Segmentation

Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00511 · Cited by 1,587 · see citing works

ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification

Desplanques, B., Thienpondt, J., & Demuynck, K. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2650 · Cited by 1,568 · see citing works

The iNaturalist Species Classification and Detection Dataset

Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00914 · Cited by 1,451 · see citing works

Deep Residual Shrinkage Networks for Fault Diagnosis

Zhao, M., Zhong, S., Fu, X., Tang, B., & Pecht, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2943898 · Cited by 1,447 · see citing works

Attentional Feature Fusion

Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00360 · Cited by 1,274 · see citing works

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Improved Knowledge Distillation via Teacher Assistant

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An Analysis of Scale Invariance in Object Detection - SNIP

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Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks

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A survey on deep learning for multimodal data fusion

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CGNet: A Light-Weight Context Guided Network for Semantic Segmentation

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Deep neural network models for computational histopathology: A survey

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UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

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Mobile-Former: Bridging MobileNet and Transformer

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Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking

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A review of object detection based on deep learning

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Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks

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Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection

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What Makes Training Multi-modal Classification Networks Hard?

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Global Second-Order Pooling Convolutional Networks

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A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

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XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

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Understanding Robustness of Transformers for Image Classification

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Salient Object Detection via Integrity Learning

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Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation

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Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection

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Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

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Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT

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Is Pseudo-Lidar needed for Monocular 3D Object detection?

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MegDet: A Large Mini-Batch Object Detector

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Dual-level Collaborative Transformer for Image Captioning

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ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild

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Data Uncertainty Learning in Face Recognition

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

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Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

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Rethinking Bottleneck Structure for Efficient Mobile Network Design

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Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

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RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

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Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

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Deep Learning for Generic Object Detection: A Survey

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DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors

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Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

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Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition

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SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

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Skin disease diagnosis with deep learning: a review

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Replay and Synthetic Speech Detection with Res2Net Architecture

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Striking the Right Balance With Uncertainty

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Bringing ai to edge: from deep learning’s perspective

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X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

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Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

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PAIP 2019: Liver cancer segmentation challenge

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Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors

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ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

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Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database

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A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects

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Improved RawNet with Feature Map Scaling for Text-Independent Speaker Verification Using Raw Waveforms

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Large-Scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification

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Myofibril and mitochondria morphogenesis are coordinated by a mechanical feedback mechanism in muscle

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Multi-Modal Neuroimaging Feature Fusion for Diagnosis of Alzheimer’s Disease

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Fine-Grained Urban Flow Prediction

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Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

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Fdftnet: facing off fake images using fake detection fine-tuning network

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A survey of model compression strategies for object detection

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Audio-Visual Deep Neural Network for Robust Person Verification

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Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

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Self-Supervised Representation Learning for Ultrasound Video

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MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance

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Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector

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Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

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A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca Disease Based on a Compressed CNN

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Fast and accurate spacecraft pose estimation from single shot space imagery using box reliability and keypoints existence judgments

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Self-Supervised Contrastive Video-Speech Representation Learning for Ultrasound

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A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

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DeNovoCNN: a deep learning approach to <i>de novo</i> variant calling in next generation sequencing data

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Deep learning models for COVID-19 chest X-ray classification: preventing shortcut learning using feature Disentanglement

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Multi-Parametric Fusion of 3D Power Doppler Ultrasound for Fetal Kidney Segmentation Using Fully Convolutional Neural Networks

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Video Face Manipulation Detection Through Ensemble of CNNs

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TN‐USMA Net: Triple normalization‐based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound‐specific pretraining and meta attention

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Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

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Deep generalizable prediction of RNA secondary structure via base pair motif energy

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Susquehanna Chorale Spring Concert "Roots and Wings"

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