| Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes M Sabokrou, M Fayyaz, M Fathy, R Klette IEEE Transactions on Image Processing 26 (4), 1992-2004, 2017 | 2182 | 2017 |
| Adversarially learned one-class classifier for novelty detection M Sabokrou, M Khalooei, M Fathy, E Adeli IEEE conference on computer vision and pattern recognition, 3379-3388, 2018 | 908 | 2018 |
| Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes M Sabokrou, M Fayyaz, M Fathy, Z Moayed, R Klette Computer Vision and Image Understanding 172, 88-97, 2018 | 576 | 2018 |
| Vision-based human activity recognition: a survey DR Beddiar, B Nini, M Sabokrou, A Hadid Multimedia Tools and Applications 79 (41), 30509-30555, 2020 | 460 | 2020 |
| Driver behavior detection and classification using deep convolutional neural networks M Shahverdy, M Fathy, R Berangi, M Sabokrou Expert Systems with Applications 149, 113240, 2020 | 336 | 2020 |
| Real-time anomaly detection and localization in crowded scenes M Sabokrou, M Fathy, M Hoseini, R Klette IEEE conference on computer vision and pattern recognition, 56-62, 2015 | 258 | 2015 |
| A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges M Salehi, H Mirzaei, D Hendrycks, Y Li, MH Rohban, M Sabokrou Transaction on Machine Learning Research (TMLR), 2022 | 246 | 2022 |
| Video anomaly detection and localisation based on the sparsity and reconstruction error of auto‐encoder M Sabokrou, M Fathy, M Hoseini Electronics Letters 52 (13), 1122-1124, 2016 | 227 | 2016 |
| Lets keep it simple, using simple architectures to outperform deeper and more complex architectures SH HasanPour, M Rouhani, M Fayyaz, M Sabokrou arXiv preprint arXiv:1608.06037, 2016 | 157 | 2016 |
| Avid: Adversarial visual irregularity detection M Sabokrou, M Pourreza, M Fayyaz, R Entezari, M Fathy, J Gall, E Adeli Asian Conference on Computer Vision, 488-505, 2018 | 131 | 2018 |
| Deep end-to-end one-class classifier M Sabokrou, M Fathy, G Zhao, E Adeli IEEE transactions on neural networks and learning systems 32 (2), 675-684, 2020 | 113 | 2020 |
| G2D: Generate to Detect Anomaly M Pourreza, B Mohammadi, M Khaki, S Bouindour, H Snoussi, ... IEEE/CVF Winter Conference on Applications of Computer Vision, 2003-2012, 2021 | 91 | 2021 |
| STFCN: spatio-temporal FCN for semantic video segmentation M Fayyaz, MH Saffar, M Sabokrou, M Fathy, R Klette, F Huang ACCV, 2016 | 91 | 2016 |
| Sign Language Production: A Review R Rastgoo, K Kiani, S Escalera, M Sabokrou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 84 | 2021 |
| Code-bridged classifier (cbc): A low or negative overhead defense for making a cnn classifier robust against adversarial attacks F Behnia, A Mirzaeian, M Sabokrou, S Manoj, T Mohsenin, ... 2020 21st International Symposium on Quality Electronic Design (ISQED), 27-32, 2020 | 53* | 2020 |
| Autoids: auto-encoder based method for intrusion detection system M Gharib, B Mohammadi, SH Dastgerdi, M Sabokrou arXiv preprint arXiv:1911.03306, 2019 | 53 | 2019 |
| Towards principled design of deep convolutional networks: introducing SimpNet SH Hasanpour, M Rouhani, M Fayyaz, M Sabokrou, E Adeli arXiv preprint arXiv:1802.06205, 2018 | 53 | 2018 |
| Fake it until you make it: Towards accurate near-distribution novelty detection H Mirzaei, M Salehi, S Shahabi, E Gavves, CGM Snoek, M Sabokrou, ... The eleventh international conference on learning representations, 2022 | 52* | 2022 |
| Deep-hr: Fast heart rate estimation from face video under realistic conditions M Sabokrou, M Pourreza, X Li, M Fathy, G Zhao Expert Systems with Applications 186, 115596, 2021 | 52 | 2021 |
| Image/video deep anomaly detection: A survey B Mohammadi, M Fathy, M Sabokrou arXiv preprint arXiv:2103.01739, 2021 | 52 | 2021 |
Mahmood FathyEmeritus professor computer Engineering, Iran university of science and technology在 iust.ac.ir 的电子邮件经过验证