334 citations · 1.2k across the 64 of their papers we have counts for
73 papers
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 (…
Hierarchical reinforcement learning for in-hand robotic manipulation using Davenport chained rotations
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
End-to-end reinforcement learning techniques are among the most successful methods for robotic manipulation tasks. However, the training time required to find a good policy capable…
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
Paul Albert, Eric Arazo, Tarun Krishna +2
Designing robust algorithms capable of training accurate neural networks on uncurated datasets from the web has been the subject of much research as it reduces the need for time co…
Motion Aware Self-Supervision for Generic Event Boundary Detection
Ayush K. Rai, Tarun Krishna, Julia Dietlmeier +3
The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling…
BaseTransformers: Attention over base data-points for One Shot Learning
Mayug Maniparambil, Kevin McGuinness, Noel O'Connor
Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled exampl…
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
Sidra Aleem, Teerath Kumar, Suzanne Little +3
Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learn…
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers
Ashish Singh, Antonio Bevilacqua, Thach Le Nguyen +6
Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorpora…
Towards advanced robotic manipulation
Francisco Roldan Sanchez, Stephen Redmond, Kevin McGuinness +1
Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world app…
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Carles Garcia-Cabrera, Eric Arazo, Kathleen M. Curran +2
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and…
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets
Paul Albert, Eric Arazo, Noel E. O'Connor +1
Using search engines for web image retrieval is a tempting alternative to manual curation when creating an image dataset, but their main drawback remains the proportion of incorrec…
Dynamic Channel Selection in Self-Supervised Learning
Tarun Krishna, Ayush K. Rai, Yasser A. D. Djilali +3
Whilst computer vision models built using self-supervised approaches are now commonplace, some important questions remain. Do self-supervised models learn highly redundant channel…
Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video
Eric Arazo, Robin Aly, Kevin McGuinness
Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses. Early lameness detection helps farmers ad…
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy +7
This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipula…
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition…
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk producti…
Synthetic data for unsupervised polyp segmentation
Enric Moreu, Kevin McGuinness, Noel E. O'Connor
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…