no code implementations • 18 Dec 2024 • Sen Yan, David J. O'Connor, Xiaojun Wang, Noel E. O'Connor, Alan F. Smeaton, Mingming Liu
The results indicate that diffusion methods with external features achieved the highest F1 score, reaching 0. 9486 (Accuracy: 94. 26%, Precision: 94. 42%, Recall: 94. 82%), with ensemble models achieving the highest accuracy of 94. 82%, illustrating that good performance can be obtained despite a high missing data rate.
no code implementations • 12 Dec 2024 • Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira, Mayug Maniparambil, Catarina Barata, Noel E. O'Connor
When applied to the Conceptual Captions dataset for creating gender counterfactuals, our method results in higher visual and semantic fidelity than state-of-the-art alternatives, while maintaining the performance of models trained using only real data on non-human-centric tasks.
no code implementations • 28 Sep 2024 • Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, Noel E. O'Connor
However, this practice has left powerful unimodal encoders for both vision and language underutilized in multimodal applications which raises a key question: Is there a plausible way to connect unimodal backbones for zero-shot vision-language tasks?
no code implementations • 21 Jul 2024 • Mohamed Allam, Noureddine Boujnah, Noel E. O'Connor, Mingming Liu
This paper proposes a framework for time series generation built to investigate anomaly detection in cloud microservices.
1 code implementation • 8 Jul 2024 • Paul Albert, Jack Valmadre, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples.
no code implementations • 10 Apr 2024 • Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran, Noel E. O'Connor
This study aims to explore the untapped potential of attention mechanisms incorporated with a deep learning model within the context of the CMR reconstruction problem.
1 code implementation • 9 Apr 2024 • Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Guenole Silvestre, Kathleen Curran, Noel E. O'Connor, Suzanne Little
To adapt SAM to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available.
1 code implementation • CVPR 2024 • Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Mohamed El Amine Seddik, Karttikeya Mangalam, Noel E. O'Connor
In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training.
no code implementations • 27 Nov 2023 • Ayush K. Rai, Tarun Krishna, Feiyan Hu, Alexandru Drimbarean, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor
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 with normal instances while test data contains both normal and anomalous instances.
no code implementations • 22 Jul 2023 • Enric Moreu, Eric Arazo, Kevin McGuinness, Noel E. O'Connor
To address both challenges, we leverage synthetic data and propose an end-to-end model for polyp segmentation that integrates real and synthetic data to artificially increase the size of the datasets and aid the training when unlabeled samples are available.
1 code implementation • 21 Jul 2023 • Mayug Maniparambil, Chris Vorster, Derek Molloy, Noel Murphy, Kevin McGuinness, Noel E. O'Connor
Meanwhile, recent developments in generative pretrained models like GPT-4 mean they can be used as advanced internet search tools.
no code implementations • 20 Jul 2023 • Enric Moreu, Eric Arazo, Kevin McGuinness, Noel E. O'Connor
We take advantage of recent one-sided translation models because they use significantly less memory, allowing us to add a segmentation model in the training loop.
no code implementations • 9 May 2023 • Enric Moreu, Alex Martinelli, Martina Naughton, Philip Kelly, Noel E. O'Connor
We introduce a new unsupervised domain adaptation technique that converts images from the synthetic domain into the real-world domain.
1 code implementation • 30 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Francisco Roldan Sanchez, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond
However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory.
no code implementations • 27 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Nico Gürtler, Felix Widmaier, Francisco Roldan Sanchez, Stephen J. Redmond
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems.
1 code implementation • 11 Oct 2022 • Ayush K. Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor
In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task.
2 code implementations • 10 Oct 2022 • Paul Albert, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets.
no code implementations • 20 Sep 2022 • Carles Garcia-Cabrera, Eric Arazo, Kathleen M. Curran, Noel E. O'Connor, Kevin McGuinness
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 functional analysis of those tissues.
1 code implementation • 25 Jul 2022 • Tarun Krishna, Ayush K. Rai, Yasser A. D. Djilali, Alan F. Smeaton, Kevin McGuinness, Noel E. O'Connor
Currently, convnets pre-trained with self-supervision have obtained comparable performance on downstream tasks in comparison to their supervised counterparts in computer vision.
1 code implementation • 4 Jul 2022 • Paul Albert, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
These noisy samples have been evidenced by previous works to be a mixture of in-distribution (ID) samples, assigned to the incorrect category but presenting similar visual semantics to other classes in the dataset, and out-of-distribution (OOD) images, which share no semantic correlation with any category from the dataset.
no code implementations • 20 Apr 2022 • Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling H. O'Connor, Noel E. O'Connor, Kevin McGuinness
Sward species composition estimation is a tedious one.
1 code implementation • 18 Apr 2022 • Paul Albert, Mohamed Saadeldin, Badri Narayanan, Jaime Fernandez, Brian Mac Namee, Deirdre Hennessey, Noel E. O'Connor, Kevin McGuinness
In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage.
1 code implementation • 17 Feb 2022 • Enric Moreu, Kevin McGuinness, Noel E. O'Connor
Deep learning has shown excellent performance in analysing medical images.
1 code implementation • 17 Feb 2022 • Enric Moreu, Kevin McGuinness, Diego Ortego, Noel E. O'Connor
We introduce a domain randomization approach for object counting based on synthetic datasets that are quick and inexpensive to generate.
1 code implementation • LREC 2022 • Luis Lebron, Yvette Graham, Kevin McGuinness, Konstantinos Kouramas, Noel E. O'Connor
The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks.
no code implementations • 17 Nov 2021 • Julia Dietlmeier, Feiyan Hu, Frances Ryan, Noel E. O'Connor, Kevin McGuinness
We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37. 43% in mAP and a gain of 30. 22% in Rank1 accuracy.
1 code implementation • 27 Oct 2021 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
We suggest that, given a specific budget, the best course of action is to disregard the importance and introduce adequate data augmentation; e. g. when reducing the budget to a 30% in CIFAR-10/100, RICAP data augmentation maintains accuracy, while importance sampling does not.
no code implementations • 21 Apr 2021 • Zhengyong Chen, Hongde Wu, Noel E. O'Connor, Mingming Liu
Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management.
no code implementations • 15 Apr 2021 • Hongde Wu, Noel E. O'Connor, Jennifer Bruton, Mingming Liu
In this paper, we investigate a key problem of Internet of Things (IoT) applications in practice.
no code implementations • 9 Feb 2021 • Marc Górriz, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak
Simplifications include a framework for reducing the overhead of the convolutional operations, a simplified cross-component processing model integrated into the original architecture, and a methodology to perform integer-precision approximations with the aim to obtain fast and hardware-aware implementations.
1 code implementation • ICCV 2021 • Yasser Abdelaziz Dahou Djilali, Tarun Krishna, Kevin McGuinness, Noel E. O'Connor
This performance is achieved using an encoder that is trained in a completely unsupervised way and a relatively lightweight supervised decoder (3. 8 X fewer parameters in the case of the ResNet50 encoder).
no code implementations • 31 Dec 2020 • Phuc H. Le-Khac, Ayush K. Rai, Graham Healy, Alan F. Smeaton, Noel E. O'Connor
The Predicting Media Memorability task in MediaEval'20 has some challenging aspects compared to previous years.
1 code implementation • CVPR 2021 • Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples.
Ranked #22 on
Image Classification
on mini WebVision 1.0
1 code implementation • 15 Nov 2020 • Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, Xavier Serra
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 event research.
no code implementations • 13 Oct 2020 • Julia Dietlmeier, Joseph Antony, Kevin McGuinness, Noel E. O'Connor
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces.
no code implementations • 31 Jul 2020 • Feiyan Hu, Venkatesh G M, Noel E. O'Connor, Alan F. Smeaton, Suzanne Little
We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.
1 code implementation • 23 Jul 2020 • Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
We propose Reliable Label Bootstrapping (ReLaB), an unsupervised preprossessing algorithm which improves the performance of semi-supervised algorithms in extremely low supervision settings.
no code implementations • 27 Jun 2020 • Marc Górriz, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak
Neural networks can be used in video coding to improve chroma intra-prediction.
1 code implementation • 11 Jun 2020 • Luka Murn, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak
Deep learning has shown great potential in image and video compression tasks.
no code implementations • 1 May 2020 • Mark Marsden, Kevin McGuinness, Joseph Antony, Haolin Wei, Milan Redzic, Jian Tang, Zhilan Hu, Alan Smeaton, Noel E. O'Connor
This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time.
1 code implementation • 18 Dec 2019 • Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
However, we show that different noise distributions make the application of this trick less straightforward and propose to continuously relabel all images to reveal a discriminative loss against multiple distributions.
1 code implementation • 26 Aug 2019 • Marc Górriz, Marta Mrak, Alan F. Smeaton, Noel E. O'Connor
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image.
1 code implementation • 23 Aug 2019 • Marc Górriz, Joseph Antony, Kevin McGuinness, Xavier Giró-i-Nieto, Noel E. O'Connor
This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI).
no code implementations • 23 Aug 2019 • Jaynal Abedin, Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E. O'Connor, Dietrich Rebholz-Schuhmann, John Newell
Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain.
4 code implementations • 8 Aug 2019 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples.
2 code implementations • 3 Jul 2019 • Panagiotis Linardos, Eva Mohedano, Juan Jose Nieto, Noel E. O'Connor, Xavier Giro-i-Nieto, Kevin McGuinness
This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain.
no code implementations • 25 Apr 2019 • Diego Ortego, Kevin McGuinness, Juan C. SanMiguel, Eric Arazo, José M. Martínez, Noel E. O'Connor
This guiding process relies on foreground masks from independent algorithms (i. e. state-of-the-art algorithms) to implement an attention mechanism that incorporates the spatial location of foreground and background to compute their separated representations.
2 code implementations • 25 Apr 2019 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss).
Ranked #44 on
Image Classification
on Clothing1M
1 code implementation • 3 Sep 2018 • Marc Assens, Xavier Giro-i-Nieto, Kevin McGuinness, Noel E. O'Connor
We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples.
1 code implementation • 29 Nov 2017 • Eva Mohedano, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor
This work explores attention models to weight the contribution of local convolutional representations for the instance search task.
no code implementations • CVPR 2018 • Mark Marsden, Kevin McGuinness, Suzanne Little, Ciara E. Keogh, Noel E. O'Connor
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 original counting function.
1 code implementation • 11 Jul 2017 • Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor
The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes.
1 code implementation • 30 May 2017 • Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification.
no code implementations • 29 Mar 2017 • Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E. O'Connor
We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN).
4 code implementations • 4 Jan 2017 • Junting Pan, Cristian Canton Ferrer, Kevin McGuinness, Noel E. O'Connor, Jordi Torres, Elisa Sayrol, Xavier Giro-i-Nieto
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
no code implementations • 1 Dec 2016 • Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
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 (Zhang et al., 2016).
no code implementations • 9 Sep 2016 • Joseph Antony, Kevin McGuinness, Neil Welch, Joe Coyle, Andy Franklyn-Miller, Noel E. O'Connor, Kieran Moran
In this paper, we propose a method to precisely quantify the fat deposition / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis and analysis.
no code implementations • 29 Aug 2016 • Cristian Reyes, Eva Mohedano, Kevin McGuinness, Noel E. O'Connor, Xavier Giro-i-Nieto
This work presents a retrieval pipeline and evaluation scheme for the problem of finding the last appearance of personal objects in a large dataset of images captured from a wearable camera.
2 code implementations • 15 Apr 2016 • Eva Mohedano, Amaia Salvador, Kevin McGuinness, Ferran Marques, Noel E. O'Connor, Xavier Giro-i-Nieto
This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW).
no code implementations • 27 May 2015 • Carles Ventura, Xavier Giró-i-Nieto, Verónica Vilaplana, Kevin McGuinness, Ferran Marqués, Noel E. O'Connor
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.
no code implementations • 19 Aug 2014 • Eva Mohedano, Graham Healy, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor, Alan F. Smeaton
This paper explores the potential of brain-computer interfaces in segmenting objects from images.