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Deep CMST Framework for the Autonomous Recognition of Heavily Occluded and Cluttered Baggage Items from Multivendor Security Radiographs.

, , , , and . CoRR, (2019)

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GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training, , and . (2018)Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection, , and . 2019 International Joint Conference on Neural Networks (IJCNN), (July 2019)Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments., , , , and . IEEE Robotics Autom. Lett., 4 (4): 4116-4123 (2019)A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items., , , , and . CoRR, (2022)Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition., , , , and . CoRR, (2021)Towards real-time anomaly detection within X-ray security imagery: self-supervised adversarial training approach.. Durham University, UK, (2020)British Library, EThOS.Incremental Instance Segmentation for Cluttered Baggage Threat Detection., , , , , , and . CIVEMSA, page 1-6. IEEE, (2023)Autoencoders Without Reconstruction for Textural Anomaly Detection., , , and . IJCNN, page 1-8. IEEE, (2021)Anomalib: A Deep Learning Library for Anomaly Detection., , , , , and . ICIP, page 1706-1710. IEEE, (2022)Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery., , , and . IEEE Trans. Inf. Forensics Secur., 13 (9): 2203-2215 (2018)