| Video anomaly detection with NTCN-ML: A novel TCN for multi-instance learning W Shao, R Xiao, P Rajapaksha, M Wang, N Crespi, Z Luo, R Minerva Pattern Recognition 143, 109765, 2023 | 25 | 2023 |
| Covad: Content-oriented video anomaly detection using a self attention-based deep learning model W Shao, P Rajapaksha, Y Wei, D Li, N Crespi, Z Luo Virtual Reality & Intelligent Hardware 5 (1), 24-41, 2023 | 20 | 2023 |
| FJLT-FLSH: More efficient fly locality-sensitive hashing algorithm via FJLT for WMSN IoT search W Shao, R Xiao, J Huang, H Liu, X Du IEEE Internet of Things Journal 6 (4), 7122-7136, 2019 | 10 | 2019 |
| Low-latency dimensional expansion and anomaly detection empowered secure iot network W Shao, Y Wei, P Rajapaksha, D Li, Z Luo, N Crespi IEEE Transactions on Network and Service Management 20 (3), 3865-3879, 2023 | 5 | 2023 |
| Toward more efficient wmsn data search combined fjlt dimension expansion with pca dimension reduction C Xiao, W Shao, R Xiao Ieee Access 8, 104139-104147, 2020 | 2 | 2020 |
| Consistency-constrained unsupervised video anomaly detection framework based on Co-teaching S Wenhao, P Rajapaksha, N Crespi, X Zhao, M Wang, N Yin, X Liu, Z Luo Neurocomputing, 128589, 2024 | 1 | 2024 |
| Improving cross-lingual transfer with contrastive negative learning and self-training G Li, X Zhao, AR Jafari, W Shao, R Farahbakhsh, N Crespi LREC-COLING 2024, 2024 | 1 | 2024 |
| Enhancing Video Anomaly Detection by Leveraging Advanced Deep Learning Techniques W Shao Institut Polytechnique de Paris, 2023 | 1 | 2023 |
| Detecting Potential Market Corner Risk of WTI: A Hybrid Algorithm Anomaly Detection Approach DUN LI, H Li, N Crespi, R Minerva, R Farahbakhsh, W Shao, KC Li | | 2023 |
| Detecting Potential Market Corner Risk of WTI: A Hybrid Anomaly Detection Approach D Li, D Han, N Crespi, R Minerva, W Shao, KC Li | | 2022 |