Suivre
Marcella Astrid
Titre
Citée par
Citée par
Année
Small object detection using context and attention
JS Lim, M Astrid, HJ Yoon, SI Lee
2021 international Conference on Artificial intelligence in information and …, 2021
3242021
Old is gold: Redefining the adversarially learned one-class classifier training paradigm
MZ Zaheer, J Lee, M Astrid, SI Lee
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
2822020
Claws: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection
MZ Zaheer, A Mahmood, M Astrid, SI Lee
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
1752020
Cp-decomposition with tensor power method for convolutional neural networks compression
M Astrid, SI Lee
2017 IEEE International Conference on Big Data and Smart Computing (BigComp …, 2017
1132017
Smoothmix: a simple yet effective data augmentation to train robust classifiers
JH Lee, MZ Zaheer, M Astrid, SI Lee
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
682020
Synthetic temporal anomaly guided end-to-end video anomaly detection
M Astrid, MZ Zaheer, SI Lee
Proceedings of the IEEE/CVF International Conference on Computer Vision, 207-214, 2021
652021
Learning Not to Reconstruct Anomalies
M Astrid, MZ Zaheer, JY Lee, SI Lee
The 32nd British Machine Vision Conference, 2021
572021
Cleaning label noise with clusters for minimally supervised anomaly detection
MZ Zaheer, J Lee, M Astrid, A Mahmood, SI Lee
arXiv preprint arXiv:2104.14770, 2021
472021
An anomaly detection system via moving surveillance robots with human collaboration
MZ Zaheer, A Mahmood, MH Khan, M Astrid, SI Lee
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
252021
LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection
D Nguyen, N Mejri, IP Singh, P Kuleshova, M Astrid, A Kacem, E Ghorbel, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024
22*2024
Clustering aided weakly supervised training to detect anomalous events in surveillance videos
MZ Zaheer, A Mahmood, M Astrid, SI Lee
IEEE Transactions on Neural Networks and Learning Systems, 2023
212023
Deep compression of convolutional neural networks with low‐rank approximation
M Astrid, SI Lee
ETRI journal 40 (4), 421-434, 2018
192018
Stabilizing adversarially learned one-class novelty detection using pseudo anomalies
MZ Zaheer, JH Lee, A Mahmood, M Astrid, SI Lee
IEEE Transactions on Image Processing 31, 5963-5975, 2022
162022
Pseudobound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
M Astrid, MZ Zaheer, SI Lee
Neurocomputing 534, 147-160, 2023
152023
Design and implementation of data storage system using USB flash drive in a microcontroller based data logger
O Mahendra, D Syamsi, A Ramdan, M Astrid
2015 International Conference on Automation, Cognitive Science, Optics …, 2015
142015
Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization
M Astrid, SI Lee, BS Seo
2018 20th International Conference on Advanced Communication Technology …, 2018
122018
For safer navigation: Pedestrian-view intersection classification
M Astrid, JH Lee, MZ Zaheer, JY Lee, SI Lee
2020 International Conference on Information and Communication Technology …, 2020
92020
CNN based sentence classification with semantic features using word clustering
HY Kim, J Lee, NY Yeo, M Astrid, SI Lee, YK Kim
2018 International Conference on Information and Communication Technology …, 2018
82018
Limiting reconstruction capability of autoencoders using moving backward pseudo anomalies
M Astrid, MZ Zaheer, SI Lee
2022 19th international conference on ubiquitous robots (UR), 248-251, 2022
52022
What do pedestrians see?: Visualizing pedestrian-view intersection classification
M Astrid, MZ Zaheer, JH Lee, JY Lee, SI Lee
2020 20th International Conference on Control, Automation and Systems (ICCAS …, 2020
42020
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20