Search Results for author: Steven McDonagh

Found 29 papers, 11 papers with code

Region Proposal Network Pre-Training Helps Label-Efficient Object Detection

no code implementations16 Nov 2022 Linus Ericsson, Nanqing Dong, Yongxin Yang, Ales Leonardis, Steven McDonagh

In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.

Instance Segmentation object-detection +3

Content-Diverse Comparisons improve IQA

no code implementations9 Nov 2022 William Thong, Jose Costa Pereira, Sarah Parisot, Ales Leonardis, Steven McDonagh

This restricts the diversity and number of image pairs that the model is exposed to during training.

Image Quality Assessment SSIM

CLAD: A realistic Continual Learning benchmark for Autonomous Driving

1 code implementation7 Oct 2022 Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.

Autonomous Driving Continual Learning +2

Out-of-Distribution Detection with Class Ratio Estimation

no code implementations8 Jun 2022 Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh

In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

CroMo: Cross-Modal Learning for Monocular Depth Estimation

no code implementations CVPR 2022 Yannick Verdié, Jifei Song, Barnabé Mas, Benjamin Busam, Aleš Leonardis, Steven McDonagh

Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy.

Monocular Depth Estimation

Long-tail Recognition via Compositional Knowledge Transfer

no code implementations CVPR 2022 Sarah Parisot, Pedro M. Esperanca, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li

In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer.

Transfer Learning

Spread Flows for Manifold Modelling

no code implementations29 Sep 2021 Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh

Flow-based models typically define a latent space with dimensionality identical to the observational space.

Residual Contrastive Learning: Unsupervised Representation Learning from Residuals

no code implementations29 Sep 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.

Contrastive Learning Image Reconstruction +3

On the Out-of-distribution Generalization of Probabilistic Image Modelling

2 code implementations NeurIPS 2021 Mingtian Zhang, Andi Zhang, Steven McDonagh

Out-of-distribution (OOD) detection and lossless compression constitute two problems that can be solved by the training of probabilistic models on a first dataset with subsequent likelihood evaluation on a second dataset, where data distributions differ.

Out-of-Distribution Generalization Out of Distribution (OOD) Detection

Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images

no code implementations18 Jun 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.

Contrastive Learning Demosaicking +6

On the Latent Space of Flow-based Models

no code implementations1 Jan 2021 Mingtian Zhang, Yitong Sun, Steven McDonagh, Chen Zhang

Flow-based generative models typically define a latent space with dimensionality identical to the observational space.

Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning

no code implementations1 Jan 2021 Xu Lan, Steven McDonagh, Shaogang Gong, Jiali Wang, Zhenguo Li, Sarah Parisot

Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings.

Few-Shot Learning Pseudo Label

Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping

1 code implementation ECCV 2020 Danai Triantafyllidou, Sean Moran, Steven McDonagh, Sarah Parisot, Gregory Slabaugh

Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e. g. smartphone cameras) without the need for a tripod.

Image and Video Processing

DeepLPF: Deep Local Parametric Filters for Image Enhancement

2 code implementations CVPR 2020 Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Gregory Slabaugh

We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.

Image Enhancement

A Multi-Hypothesis Approach to Color Constancy

1 code implementation CVPR 2020 Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam, Ales Leonardis, Gregory Slabaugh, Steven McDonagh

Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.

Color Constancy

CURL: Neural Curve Layers for Global Image Enhancement

3 code implementations29 Nov 2019 Sean Moran, Steven McDonagh, Gregory Slabaugh

We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.

Demosaicking Denoising +2

SteReFo: Efficient Image Refocusing with Stereo Vision

no code implementations29 Sep 2019 Benjamin Busam, Matthieu Hog, Steven McDonagh, Gregory Slabaugh

Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike.

Depth Estimation

Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem

no code implementations28 Nov 2018 Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh

In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.

Few-Shot Camera-Adaptive Color Constancy Meta-Learning

Attention U-Net: Learning Where to Look for the Pancreas

30 code implementations11 Apr 2018 Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

Brain Tumor Segmentation Image Segmentation +2

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

no code implementations4 Nov 2017 Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation.

Semantic Segmentation

3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

no code implementations19 Sep 2017 Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.

3D Reconstruction Image Reconstruction +2

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

1 code implementation28 Feb 2017 Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.

Image Registration Motion Compensation +1

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