no code implementations • 27 Nov 2023 • Ayush K. Rai, Tarun Krishna, Feiyan Hu, Alexandru Drimbarean, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor
Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances.
1 code implementation • 11 Jan 2023 • Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri, Giovanni Bellitto, Morteza Moradi, Concetto Spampinato, Kevin McGuinness
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications.
1 code implementation • 2 Oct 2022 • Ashish Singh, Antonio Bevilacqua, Thach Le Nguyen, Feiyan Hu, Kevin McGuinness, Martin OReilly, Darragh Whelan, Brian Caulfield, Georgiana Ifrim
We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors.
no code implementations • 17 Nov 2021 • Julia Dietlmeier, Feiyan Hu, Frances Ryan, Noel E. O'Connor, Kevin McGuinness
We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37. 43% in mAP and a gain of 30. 22% in Rank1 accuracy.
no code implementations • 18 Dec 2020 • Feiyan Hu, Eva Mohedano, Noel O'Connor, Kevin McGuinness
Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources.
1 code implementation • 25 Aug 2020 • Feiyan Hu, Kevin McGuinness
This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget.
no code implementations • 31 Jul 2020 • Feiyan Hu, Venkatesh G M, Noel E. O'Connor, Alan F. Smeaton, Suzanne Little
We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.