Publications

Presentation

Publications

2024

Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, and Seung-Ik Lee. “Exploiting Autoencoder’s Weakness to Generate Pseudo Anomalies”. In: Neural Comput & Applic (May 3, 2024). doi:https://doi.org/10.1007/s00521- 024-09790-z.

2023

Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, and Seung-Ik Lee. “Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveil lance Videos”. In: IEEE Transactions on Neural Networks and Learning Systems (2023). (Accepted. IF=14.255(2021)).

Seung-Min Choi, Seung-Ik Lee, Jae-Yeong Lee, and In So Kweon. “Semantic-guided de-attention with sharpened triplet marginal loss for visual place recognition”. In: Pattern Recognition 141 (Sept. 2023). doi: https://doi.org/10.1016/j.patcog.2023.109645. (IF=8.518(2021)).

Marcella Astrid, Muhammad Zaigham Zahee, and Seung-Ik Lee. “PseudoBound: Limiting the Anomaly Reconstruction Capability of One-Class Classifiers Using Pseudo Anomalies”. In: Neurocomputing 534 (May 14, 2023), pp. 147–160. doi: https://doi.org/https://doi.org/10.1016/j.neucom.2023.03.008. (IF=5.779(2021)).

Hyeon-Jae Jeong, Jubin Lee, Yu Seung Ma, and Seung-Ik Lee. “Attack Success Rate Analysis of Adversarial Patch in Physical Environment”. In: Journal of KIISE 50.2 (Feb. 2023), pp. 185–195. doi: https://doi.org/https://doi.org/10.5626/JOK.2023.50.2.185.

Marcella Astrid, Minsu Jang, and Seung-Ik Lee. “Improving discriminative capability of anomaly detector using pseudo anomalies and triplet loss”. In: KICS Winter Conference 2023 (KICS 2023) (Yong Pyong Resort, Pyeongchang, Korea). Feb. 8–10, 2023.

Assefa Seyoum Wahd, Marcella Astrid, and Seung-Ik Lee. “Low-Likelihovod EBM Samples for Out-of-Distribution Detection”. In: 35th Workshop on Image Processing and Image Understanding (IPIU 2023) (Jeju, Korea). Feb. 8–10, 2023.

2022

Marcella Astrid and Seung-Ik Lee. “Using Pseudo Anomalies to Train a Binary Classifier for Anomaly Detection”. In: Korea Software Congress 2022 (KSC 2022) (Ramada Plaza Hotel, Jeju, Korea). Dec. 20–23, 2022, pp. 818–820. [link]

Assefa Seyoum Wahd, Donghyung Kim, and Seung-Ik Lee. “Cable Instance Segmentation with Synthetic Data Generation”. In: The 21st International Conference on Control, Automation and Systems (ICCAS 2022) (Bexco, Pusan, Korea). Nov. 27– Dec. 1, 2022, pp. 1533–1538. [link]

Marcella Astrid and Seung-Ik Lee. “Constructing Three-Camera Data with OneCamera Data for Intersection Detection”. In: Conference on Information and Control Systems (CICS22 Workshop) (PyeongChang, Korea). Oct. 19–22, 2022, pp. 26–27. 

Namkyoo Kang, Seungeun Han, Seung Yeon Kim, SeungJoon Kwon, Yeongjae Choi, Yong-Tae Lee, and Seung-Ik Lee. “Driver Drowsiness Detection based on 3D Convolution Neural Network with Optimized Window Size”. In: The 13th International Conference on Information and Communication Technology Convergence (ICTC 2022) (Ramada Plaza Hotel, Jeju, Korea). Oct. 19–21, 2022, pp. 425–428.

Marcella Astrid and Seung-Ik Lee. “The Comparisons of Boundary Data Generation for Semi-Supervised Learning in Image Classification”. In: 3rd Korea Artificial Intelligence Conference (Jeju, Korea). Sept. 28–30, 2022.

Muhammad Zaigham Zaheer, Jin-Ha Lee, Arif Mahmood, Marcella Astrid, and Seung-Ik Lee. “Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies”. In: IEEE Transactions on Image Processing 31 (Sept. 12, 2022), pp. 5963–5975. doi: https://doi.org/10.1109/TIP.2022.3204217. (IF=11.041(2021)).

Marcella Astrid, Muhammad Zaigham Zaheer, and Seung-Ik Lee. "Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies". In: The 19th International Conference on Ubiquitous Robots (UR 2022) (Jeju, Korea). July 4-6, 2022. [link]

Marcella Astrid and Seung-Ik Lee. “Utilizing One-Camera Data for Three-Camera Intersection Classification”. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). June 19–24, 2022. (unpublished version).

Marcella Astrid, Muhammad Zaigham Zaheer, and Seung-Ik Lee. “Improving Anomaly Detection Model Using Prior Knowledge”. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). June 19–24, 2022. (unpublished version).

Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Segù, Fisher Yu, and Seung-Ik Lee. “Generative cooperative learning for unsupervised video anomaly detection”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Louisiana, USA). June 19–24, 2022, pp. 14744–14754. (h-5 index(2020): 299).

Marcella Astrid and Seung-Ik Lee. “Generalizing Skipping Frame Pseudo Anomaly Based Anomaly Detection Into Prediction Task”. In: THE 32nd JOINT CONFERENCE ON COMMUNICATIONS AND INFORMATION (Sokcho, Korea). Apr. 27–29, 2022. [link]

Marcella Astrid, Muhammad Zaigham Zaheer, and Seung-Ik Lee. “Semi-Supervised Learning Using Data Between Real and Noise”. In: KICS Winter Conference 2022 (KICS 2022)  (PyeongChang, Korea). Feb. 9–11, 2022, pp. 545–546. [link]