M
Mohsen Fayyaz
Researcher at University of Bonn
Publications - 34
Citations - 1869
Mohsen Fayyaz is an academic researcher from University of Bonn. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 17, co-authored 34 publications receiving 1250 citations. Previous affiliations of Mohsen Fayyaz include Malek-Ashtar University of Technology.
Papers
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Journal ArticleDOI
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
TL;DR: In this paper, a pre-trained supervised FCNets are transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes by investigating the cascaded detection as a result of reducing computation complexities.
Journal ArticleDOI
Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes
TL;DR: It is shown that the proposed novel technique, characterised by a cascade of two cascaded classifiers, performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.
Book ChapterDOI
Spatio-temporal Channel Correlation Networks for Action Classification
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Mohammad Mahdi Arzani,Rahman Yousefzadeh,Juergen Gall,Luc Van Gool +6 more
TL;DR: By fine-tuning this network, this work beats the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, and fine- Tuned on the target datasets, e.g. HMDB51/UCF101 and Kinetics.
Posted Content
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification.
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Amir Hossein Karami,Mohammad Mahdi Arzani,Rahman Yousefzadeh,Luc Van Gool +6 more
TL;DR: By finetuning this network, the proposed video convolutional network T3D outperforms the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, and finetuned on the target datasets, e.g. HMDB51/UCF101.
Posted Content
Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes.
TL;DR: An FCN-based architecture for anomaly detection and localization in crowded scenes videos is proposed, which includes two main components, one for feature representation, and one for cascaded out-layer detection.