334 citations · 1.2k across the 64 of their papers we have counts for
9 papers · 1 filter
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Ayush K. Rai, Tarun Krishna, Feiyan Hu +4
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 w…
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
Mayug Maniparambil, Chris Vorster, Derek Molloy +3
Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs a…
Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Early detection of colorectal polyps is of utmost importance for their treatment and for colorectal cancer prevention. Computer vision techniques have the potential to aid professi…
Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annot…
An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans
Sidra Aleem, Mayug Maniparambil, Suzanne Little +2
Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity predictio…
Site-specific Deep Learning Path Loss Models based on the Method of Moments
Conor Brennan, Kevin McGuinness
This paper describes deep learning models based on convolutional neural networks applied to the problem of predicting EM wave propagation over rural terrain. A surface integral equ…
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri +4
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are p…
Improving Behavioural Cloning with Positive Unlabeled Learning
Qiang Wang, Robert McCarthy, David Cordova Bulens +6
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed q…
Unifying Synergies between Self-supervised Learning and Dynamic Computation
Tarun Krishna, Ayush K Rai, Alexandru Drimbarean +5
Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings. Techniques like knowledge distillation (…