Mohammad Sabokrou

Orcid: 0000-0002-9409-2799

According to our database1, Mohammad Sabokrou authored at least 70 papers between 2015 and 2024.

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Bibliography

2024
Diverse hand gesture recognition dataset.
Multim. Tools Appl., May, 2024

STARNet: spatio-temporal aware recurrent network for efficient video object detection on embedded devices.
Mach. Vis. Appl., March, 2024

Projan: A probabilistic trojan attack on deep neural networks.
Knowl. Based Syst., 2024

Multi-modal zero-shot dynamic hand gesture recognition.
Expert Syst. Appl., 2024

A survey on recent advances in Sign Language Production.
Expert Syst. Appl., 2024

Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive Replay.
CoRR, 2024

Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation.
CoRR, 2024

Killing It With Zero-Shot: Adversarially Robust Novelty Detection.
Proceedings of the IEEE International Conference on Acoustics, 2024

Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Universal Novelty Detection Through Adaptive Contrastive Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding.
IEEE Trans. Fuzzy Syst., September, 2023

MobileDenseNet: A new approach to object detection on mobile devices.
Expert Syst. Appl., April, 2023

Class-Adaptive Sampling Policy for Efficient Continual Learning.
CoRR, 2023

Explainability of Vision Transformers: A Comprehensive Review and New Perspectives.
CoRR, 2023

Confidence-driven Sampling for Backdoor Attacks.
CoRR, 2023

IMPOSITION: Implicit Backdoor Attack through Scenario Injection.
CoRR, 2023

Global-Local Processing in Convolutional Neural Networks.
CoRR, 2023

Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis.
CoRR, 2023

Expanding Explainability Horizons: A Unified Concept-Based System for Local, Global, and Misclassification Explanations.
CoRR, 2023

Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space.
CoRR, 2023

Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets.
IEEE Access, 2023

Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Mitigating Bias: Enhancing Image Classification by Improving Model Explanations.
Proceedings of the Asian Conference on Machine Learning, 2023

2022
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges.
Trans. Mach. Learn. Res., 2022

Are Out-of-Distribution Detection Methods Reliable?
CoRR, 2022

MobileDenseNet: A new approach to object detection on mobile devices.
CoRR, 2022

Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models.
CoRR, 2022

All You Need In Sign Language Production.
CoRR, 2022

Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

Looking Back on Learned Experiences For Class/task Incremental Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Imaging Time Series for Deep Embedded Clustering: a Cryptocurrency Regime Detection Use Case.
Proceedings of the 27th International Computer Conference, Computer Society of Iran, 2022

2021
Deep End-to-End One-Class Classifier.
IEEE Trans. Neural Networks Learn. Syst., 2021

Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques.
J. Supercomput., 2021

Deep-HR: Fast heart rate estimation from face video under realistic conditions.
Expert Syst. Appl., 2021

Multi-Modal Zero-Shot Sign Language Recognition.
CoRR, 2021

ClaRe: Practical Class Incremental Learning By Remembering Previous Class Representations.
CoRR, 2021

ZS-IL: Looking Back on Learned Experiences For Zero-Shot Incremental Learning.
CoRR, 2021

Image/Video Deep Anomaly Detection: A Survey.
CoRR, 2021

G2D: Generate to Detect Anomaly.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques.
Proceedings of the IEEE Nordic Circuits and Systems Conference, NorCAS 2021, Oslo, 2021

Sign Language Production: A Review.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
Vision-based human activity recognition: a survey.
Multim. Tools Appl., 2020

Driver behavior detection and classification using deep convolutional neural networks.
Expert Syst. Appl., 2020

Cluster-Based Partitioning of Convolutional Neural Networks, A Solution for Computational Energy and Complexity Reduction.
CoRR, 2020

Learning Diverse Latent Representations for Improving the Resilience to Adversarial Attacks.
CoRR, 2020

Robustification of Segmentation Models Against Adversarial Perturbations in Medical Imaging.
Proceedings of the Predictive Intelligence in Medicine - Third International Workshop, 2020

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks.
Proceedings of the 21st International Symposium on Quality Electronic Design, 2020

2019
AutoIDS: Auto-encoder Based Method for Intrusion Detection System.
CoRR, 2019

AVD: Adversarial Video Distillation.
CoRR, 2019

Generative Adversarial Irregularity Detection in Mammography Images.
Proceedings of the Predictive Intelligence in Medicine - Second International Workshop, 2019

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks.
Proceedings of the 44th IEEE Conference on Local Computer Networks, 2019

Self-Supervised Representation Learning via Neighborhood-Relational Encoding.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Unsupervised Feature Ranking and Selection Based on Autoencoders.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes.
Comput. Vis. Image Underst., 2018

Online Signature Verification using Deep Representation: A new Descriptor.
CoRR, 2018

Semantic Video Segmentation: A Review on Recent Approaches.
CoRR, 2018

Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet.
CoRR, 2018

Sub-word based Persian OCR Using Auto-Encoder Features and Cascade Classifier.
Proceedings of the 9th International Symposium on Telecommunications, 2018

Adversarially Learned One-Class Classifier for Novelty Detection.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

AVID: Adversarial Visual Irregularity Detection.
Proceedings of the Computer Vision - ACCV 2018, 2018

2017
Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes.
IEEE Trans. Image Process., 2017

Fast and accurate detection and localization of abnormal behavior in crowded scenes.
Mach. Vis. Appl., 2017

2016
Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes.
CoRR, 2016

Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures.
CoRR, 2016

An Evolvable Fuzzy Logic System for handoff management in heterogeneous Wireless Networks.
CoRR, 2016

STFCN: Spatio-Temporal FCN for Semantic Video Segmentation.
CoRR, 2016

STFCN: Spatio-Temporal Fully Convolutional Neural Network for Semantic Segmentation of Street Scenes.
Proceedings of the Computer Vision - ACCV 2016 Workshops, 2016

2015
A Novel Approach For Finger Vein Verification Based on Self-Taught Learning.
CoRR, 2015

Feature Representation for Online Signature Verification.
CoRR, 2015

Real-time anomaly detection and localization in crowded scenes.
Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015


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