Noel E. O'Connor

research

∙ 07/22/2023

Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data

Early detection of colorectal polyps is of utmost importance for their t...

0 Enric Moreu, et al. ∙

research

∙ 07/21/2023

Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts

Contrastive pretrained large Vision-Language Models (VLMs) like CLIP hav...

0 Mayug Maniparambil, et al. ∙

research

∙ 07/20/2023

Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention

Deep learning has shown excellent performance in analysing medical image...

0 Enric Moreu, et al. ∙

research

∙ 05/09/2023

Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels

Accurate product information is critical for e-commerce stores to allow ...

0 Enric Moreu, et al. ∙

research

∙ 01/27/2023

Behaviour Discriminator: A Simple Data Filtering Method to Improve Offline Policy Learning

This paper studies the problem of learning a control policy without the ...

0 Qiang Wang, et al. ∙

research

∙ 01/22/2023

Unifying Synergies between Self-supervised Learning and Dynamic Computation

Self-supervised learning (SSL) approaches have made major strides forwar...

19 Tarun Krishna, et al. ∙

research

∙ 10/11/2022

Motion Aware Self-Supervision for Generic Event Boundary Detection

The task of Generic Event Boundary Detection (GEBD) aims to detect momen...

26 Ayush K. Rai, et al. ∙

research

∙ 10/10/2022

Is your noise correction noisy? PLS: Robustness to label noise with two stage detection

Designing robust algorithms capable of training accurate neural networks...

20 Paul Albert, et al. ∙

research

∙ 09/20/2022

Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Methods that are resilient to artifacts in the cardiac magnetic resonanc...

16 Carles Garcia-Cabrera, et al. ∙

research

∙ 07/25/2022

Dynamic Channel Selection in Self-Supervised Learning

Whilst computer vision models built using self-supervised approaches are...

19 Tarun Krishna, et al. ∙

research

∙ 07/04/2022

Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets

Using search engines for web image retrieval is a tempting alternative t...

0 Paul Albert, et al. ∙

research

∙ 04/20/2022

Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation

Sward species composition estimation is a tedious one. Herbage must be c...

4 Paul Albert, et al. ∙

research

∙ 04/18/2022

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Herbage mass yield and composition estimation is an important tool for d...

13 Paul Albert, et al. ∙

research

∙ 02/17/2022

Synthetic data for unsupervised polyp segmentation

Deep learning has shown excellent performance in analysing medical image...

9 Enric Moreu, et al. ∙

research

∙ 02/17/2022

Domain Randomization for Object Counting

Recently, the use of synthetic datasets based on game engines has been s...

9 Enric Moreu, et al. ∙

research

∙ 01/25/2022

BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment

Evaluating video captioning systems is a challenging task as there are m...

3 Luis Lebron, et al. ∙

research

∙ 11/17/2021

Improving Person Re-Identification with Temporal Constraints

In this paper we introduce an image-based person re-identification datas...

5 Julia Dietlmeier, et al. ∙

research

∙ 10/27/2021

How Important is Importance Sampling for Deep Budgeted Training?

Long iterative training processes for Deep Neural Networks (DNNs) are co...

4 Eric Arazo, et al. ∙

research

∙ 06/18/2021

Discerning Generic Event Boundaries in Long-Form Wild Videos

Detecting generic, taxonomy-free event boundaries invideos represents a ...

2 Ayush K. Rai, et al. ∙

research

∙ 04/21/2021

A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

Accurately forecasting transportation demand is crucial for efficient ur...

0 Zhengyong Chen, et al. ∙

research

∙ 02/09/2021

Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding

Neural networks can be successfully used to improve several modules of a...

14 Marc Gorriz, et al. ∙

research

∙ 12/31/2020

Investigating Memorability of Dynamic Media

The Predicting Media Memorability task in MediaEval'20 has some challeng...

0 Phuc H. Le-Khac, et al. ∙

research

∙ 12/08/2020

Multi-Objective Interpolation Training for Robustness to Label Noise

Deep neural networks trained with standard cross-entropy loss memorize n...

10 Diego Ortego, et al. ∙

research

∙ 11/15/2020

Unsupervised Contrastive Learning of Sound Event Representations

Self-supervised representation learning can mitigate the limitations in ...

5 Eduardo Fonseca, et al. ∙

research

∙ 10/13/2020

How important are faces for person re-identification?

This paper investigates the dependence of existing state-of-the-art pers...

3 Julia Dietlmeier, et al. ∙

research

∙ 07/31/2020

Utilising Visual Attention Cues for Vehicle Detection and Tracking

Advanced Driver-Assistance Systems (ADAS) have been attracting attention...

1 Feiyan Hu, et al. ∙

research

∙ 07/23/2020

ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning

Reducing the amount of labels required to trainconvolutional neural netw...

9 Paul Albert, et al. ∙

research

∙ 06/27/2020

Chroma Intra Prediction with attention-based CNN architectures

Neural networks can be used in video coding to improve chroma intra-pred...

2 Marc Gorriz, et al. ∙

research

∙ 06/11/2020

Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding

Deep learning has shown great potential in image and video compression t...

6 Luka Murn, et al. ∙

research

∙ 05/01/2020

Investigating Class-level Difficulty Factors in Multi-label Classification Problems

This work investigates the use of class-level difficulty factors in mult...

6 Mark Marsden, et al. ∙

research

∙ 12/18/2019

Towards Robust Learning with Different Label Noise Distributions

Noisy labels are an unavoidable consequence of automatic image labeling ...

20 Diego Ortego, et al. ∙

research

∙ 08/26/2019

End-to-End Conditional GAN-based Architectures for Image Colourisation

In this work recent advances in conditional adversarial networks are inv...

17 Marc Gorriz, et al. ∙

research

∙ 08/23/2019

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Knee osteoarthritis (KOA) is a disease that impairs knee function and ca...

7 Jaynal Abedin, et al. ∙

research

∙ 08/23/2019

Assessing Knee OA Severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN)...

16 Marc Gorriz, et al. ∙

research

∙ 08/23/2019

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

This chapter presents the investigations and the results of feature lear...

3 Joseph Antony, et al. ∙

research

∙ 08/08/2019

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Semi-supervised learning, i.e. jointly learning from labeled an unlabele...

11 Eric Arazo, et al. ∙

research

∙ 07/03/2019

Temporal recurrences for video saliency prediction

This paper investigates modifying an existing neural network architectur...

1 Panagiotis Linardos, et al. ∙

research

∙ 07/03/2019

Simple vs complex temporal recurrences for video saliency prediction

This paper investigates modifying an existing neural network architectur...

1 Panagiotis Linardos, et al. ∙

research

∙ 04/25/2019

On guiding video object segmentation

This paper presents a novel approach for segmenting moving objects in un...

6 Diego Ortego, et al. ∙

research

∙ 04/25/2019

Unsupervised label noise modeling and loss correction

Despite being robust to small amounts of label noise, convolutional neur...

12 Eric Arazo, et al. ∙

research

∙ 09/03/2018

PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks

We introduce PathGAN, a deep neural network for visual scanpath predicti...

0 Marc Assens, et al. ∙

research

∙ 11/29/2017

Saliency Weighted Convolutional Features for Instance Search

This work explores attention models to weight the contribution of local ...

0 Eva Mohedano, et al. ∙

research

∙ 11/15/2017

People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting

In this paper we propose a technique to adapt a convolutional neural net...

0 Mark Marsden, et al. ∙

research

∙ 07/11/2017

SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes

We introduce SaltiNet, a deep neural network for scanpath prediction tra...

0 Marc Assens, et al. ∙

research

∙ 05/30/2017

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification

In this paper we propose ResnetCrowd, a deep residual architecture for s...

1 Mark Marsden, et al. ∙

research

∙ 03/29/2017

Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks

This paper introduces a new approach to automatically quantify the sever...

0 Joseph Antony, et al. ∙

research

∙ 01/04/2017

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

We introduce SalGAN, a deep convolutional neural network for visual sali...

0 Junting Pan, et al. ∙

research

∙ 12/01/2016

Fully Convolutional Crowd Counting On Highly Congested Scenes

In this paper we advance the state-of-the-art for crowd counting in high...

0 Mark Marsden, et al. ∙

research

∙ 09/09/2016

An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI

In this paper we present an interactive tool that can be used to quantif...

0 Joseph Antony, et al. ∙

research

∙ 09/08/2016

Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks

This paper proposes a new approach to automatically quantify the severit...

0 Joseph Antony, et al. ∙

Noel E. O'Connor

Featured Co-authors