Pseudo-labeling and confirmation bias in deep semi-supervised learning E Arazo, D Ortego, P Albert, NE O’Connor, K McGuinness 2020 International joint conference on neural networks (IJCNN), 1-8, 2020 | 843 | 2020 |
Unsupervised label noise modeling and loss correction E Arazo, D Ortego, P Albert, N O’Connor, K McGuinness International conference on machine learning, 312-321, 2019 | 638 | 2019 |
Shallow and deep convolutional networks for saliency prediction J Pan, E Sayrol, X Giro-i-Nieto, K McGuinness, NE O'Connor Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 558 | 2016 |
Salgan: Visual saliency prediction with generative adversarial networks J Pan, CC Ferrer, K McGuinness, NE O'Connor, J Torres, E Sayrol, ... arXiv preprint arXiv:1701.01081, 2017 | 485 | 2017 |
A comparative evaluation of interactive segmentation algorithms K McGuinness, NE O’connor Pattern Recognition 43 (2), 434-444, 2010 | 385 | 2010 |
Event detection in field sports video using audio-visual features and a support vector machine DA Sadlier, NE O'Connor IEEE Transactions on Circuits and Systems for Video Technology 15 (10), 1225 …, 2005 | 335 | 2005 |
A multiscale representation method for nonrigid shapes with a single closed contour T Adamek, NE O'Connor IEEE Transactions on Circuits and Systems for Video Technology 14 (5), 742-753, 2004 | 328 | 2004 |
Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks J Antony, K McGuinness, NE O'Connor, K Moran 2016 23rd international conference on pattern recognition (ICPR), 1195-1200, 2016 | 297 | 2016 |
Evaluating a dancer's performance using kinect-based skeleton tracking DS Alexiadis, P Kelly, P Daras, NE O'Connor, T Boubekeur, MB Moussa Proceedings of the 19th ACM international conference on Multimedia, 659-662, 2011 | 257 | 2011 |
Bags of local convolutional features for scalable instance search E Mohedano, K McGuinness, NE O'Connor, A Salvador, F Marques, ... Proceedings of the 2016 ACM on international conference on multimedia …, 2016 | 197 | 2016 |
Classification of sporting activities using smartphone accelerometers E Mitchell, D Monaghan, NE O'Connor Sensors 13 (4), 5317-5337, 2013 | 194 | 2013 |
Fully convolutional crowd counting on highly congested scenes M Marsden, K McGuinness, S Little, NE O'Connor arXiv preprint arXiv:1612.00220, 2016 | 192 | 2016 |
Touch screens for the older user N Caprani, NE O’Connor, C Gurrin Assistive technologies 1, 2012 | 188 | 2012 |
Passively recognising human activities through lifelogging AR Doherty, N Caprani, CÓ Conaire, V Kalnikaite, C Gurrin, AF Smeaton, ... Computers in human behavior 27 (5), 1948-1958, 2011 | 180 | 2011 |
Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks J Antony, K McGuinness, K Moran, NE O’Connor Machine Learning and Data Mining in Pattern Recognition: 13th International …, 2017 | 177 | 2017 |
Resnetcrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification M Marsden, K McGuinness, S Little, NE O'Connor 2017 14th IEEE international conference on advanced video and signal based …, 2017 | 157 | 2017 |
Word matching using single closed contours for indexing handwritten historical documents T Adamek, NE O’Connor, AF Smeaton International Journal of Document Analysis and Recognition (IJDAR) 9, 153-165, 2007 | 152 | 2007 |
The convergence of virtual reality and social networks: threats to privacy and autonomy F O’Brolcháin, T Jacquemard, D Monaghan, N O’Connor, P Novitzky, ... Science and engineering ethics 22, 1-29, 2016 | 141 | 2016 |
Detector adaptation by maximising agreement between independent data sources CO Conaire, NE O'Connor, AF Smeaton 2007 IEEE conference on computer vision and pattern recognition, 1-6, 2007 | 139 | 2007 |
Automatic TV advertisement detection from MPEG bitstream DA Sadlier, S Marlow, N O'Connor, N Murphy Pattern Recognition 35 (12), 2719-2726, 2002 | 137 | 2002 |