334 citations · 1.2k across the 64 of their papers we have counts for
9 papers · 1 filter
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates
Feiyan Hu, Eva Mohedano, Noel O'Connor +1
Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources. This paper a…
Multi-Objective Interpolation Training for Robustness to Label Noise
Diego Ortego, Eric Arazo, Paul Albert +2
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robu…
ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos
Yasser Dahou, Marouane Tliba, Kevin McGuinness +1
The spherical domain representation of 360 video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV). Mode…
Unsupervised Contrastive Learning of Sound Event Representations
Eduardo Fonseca, Diego Ortego, Kevin McGuinness +2
Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound ev…
How important are faces for person re-identification?
Julia Dietlmeier, Joseph Antony, Kevin McGuinness +1
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blu…
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset
David Azcona, Kevin McGuinness, Alan F. Smeaton
This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and…
FastSal: a Computationally Efficient Network for Visual Saliency Prediction
Feiyan Hu, Kevin McGuinness
This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budge…
Reliable Label Bootstrapping for Semi-Supervised Learning
Paul Albert, Diego Ortego, Eric Arazo +2
Reducing the amount of labels required to train convolutional neural networks without performance degradation is key to effectively reduce human annotation efforts. We propose Reli…
Investigating Class-level Difficulty Factors in Multi-label Classification Problems
Mark Marsden, Kevin McGuinness, Joseph Antony +6
This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time. Four class-level difficulty factors are proposed: freque…