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 • 22 Jan 2023 • Tarun Krishna, Ayush K Rai, Alexandru Drimbarean, Eric Arazo, Paul Albert, Alan F Smeaton, Kevin McGuinness, Noel E O'Connor
Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings.
1 code implementation • 11 Oct 2022 • Ayush K. Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor
In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task.
2 code implementations • 10 Oct 2022 • Paul Albert, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets.
1 code implementation • 25 Jul 2022 • Tarun Krishna, Ayush K. Rai, Yasser A. D. Djilali, Alan F. Smeaton, Kevin McGuinness, Noel E. O'Connor
Currently, convnets pre-trained with self-supervision have obtained comparable performance on downstream tasks in comparison to their supervised counterparts in computer vision.
no code implementations • 18 Jun 2021 • Ayush K Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F Smeaton, Noel E O'Connor
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding.
no code implementations • 30 Apr 2021 • Tarun Krishna, Kevin McGuinness, Noel O'Connor
In this work, we evaluate contrastive models for the task of image retrieval.
1 code implementation • ICCV 2021 • Yasser Abdelaziz Dahou Djilali, Tarun Krishna, Kevin McGuinness, Noel E. O'Connor
This performance is achieved using an encoder that is trained in a completely unsupervised way and a relatively lightweight supervised decoder (3. 8 X fewer parameters in the case of the ResNet50 encoder).