1 code implementation • 3 Jan 2024 • Joao P. C. Bertoldo, Dick Ameln, Ashwin Vaidya, Samet Akçay
Recent advances in visual anomaly detection research have seen AUROC and AUPRO scores on public benchmark datasets such as MVTec and VisA converge towards perfect recall, giving the impression that these benchmarks are near-solved.
no code implementations • 19 Apr 2020 • Seyma Yucer, Samet Akçay, Noura Al-Moubayed, Toby P. Breckon
Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society.
no code implementations • 17 Sep 2019 • Naif Alshammari, Samet Akçay, Toby P. Breckon
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e. g. foggy weather), which results in poor outdoor scene visibility.
no code implementations • 10 Apr 2019 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign).
2 code implementations • 25 Jan 2019 • Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.