7 citations · 18 across the 8 of their papers we have counts for
9 papers
Constructing a meta-learner for unsupervised anomaly detection
Małgorzata Gutowska, Suzanne Little, Andrew McCarren
Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools. Due to the diversity of problems, no…
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…
TrollsWithOpinion: A Dataset for Predicting Domain-specific Opinion Manipulation in Troll Memes
Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan +2
Research into the classification of Image with Text (IWT) troll memes has recently become popular. Since the online community utilizes the refuge of memes to express themselves, th…
Utilising Visual Attention Cues for Vehicle Detection and Tracking
Feiyan Hu, Venkatesh G M, Noel E. O'Connor +2
Advanced Driver-Assistance Systems (ADAS) have been attracting attention from many researchers. Vision-based sensors are the closest way to emulate human driver visual behavior whi…
MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation
Panagiotis Linardos, Suzanne Little, Kevin McGuinness
This work tackles the Pixel Privacy task put forth by MediaEval 2019. Our goal is to manipulate images in a way that conceals them from automatic scene classifiers while preserving…
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
Mark Marsden, Kevin McGuinness, Suzanne Little +2
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the ori…
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
Mark Marsden, Kevin McGuinness, Suzanne Little +1
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification. To train and…
Fully Convolutional Crowd Counting On Highly Congested Scenes
Mark Marsden, Kevin McGuinness, Suzanne Little +1
In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Z…
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection
M. Marsden, K. McGuinness, S. Little +1
This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimen…