Search Results for author: Steven McDonagh

Found 33 papers, 13 papers with code

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.

Ranked #8 on Image Enhancement on MIT-Adobe 5k (SSIM on proRGB metric)

Image Enhancement

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

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

Multi-task Learning with 3D-Aware Regularization

1 code implementation2 Oct 2023 Wei-Hong Li, Steven McDonagh, Ales Leonardis, Hakan Bilen

Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.

Depth Estimation Multi-Task Learning +1

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

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 +3

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

no code implementations16 Nov 2022 Nanqing Dong, Linus Ericsson, 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 +4

Learning to Name Classes for Vision and Language Models

no code implementations CVPR 2023 Sarah Parisot, Yongxin Yang, Steven McDonagh

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content.

Descriptive Image Classification +4

Optimisation-Based Multi-Modal Semantic Image Editing

no code implementations28 Nov 2023 Bowen Li, Yongxin Yang, Steven McDonagh, Shifeng Zhang, Petru-Daniel Tudosiu, Sarah Parisot

Image editing affords increased control over the aesthetics and content of generated images.

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