Search Results for author: Ales Leonardis

Found 38 papers, 11 papers with code

More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning

1 code implementation ECCV 2020 Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars

Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.

Incremental Learning

Wavelet-Based Network For High Dynamic Range Imaging

no code implementations3 Aug 2021 Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan

The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.

Optical Flow Estimation

Residual Contrastive Learning for Joint Demosaicking and Denoising

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

The breakthrough of contrastive learning (CL) has fueled the recent success of self-supervised learning (SSL) in high-level vision tasks on RGB images.

Contrastive Learning Demosaicking +2

Learning a Model-Driven Variational Network for Deformable Image Registration

no code implementations25 May 2021 Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan

We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).

Denoising Image Registration

Wavelet-Based Dual-Branch Network for Image Demoireing

no code implementations14 Jul 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

1 code implementation CVPR 2020 Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.

Continual Learning Domain Adaptation +2

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

1 code implementation CVPR 2020 Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Ales Leonardis

Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.

6D Pose Estimation 6D Pose Estimation using RGB

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

AIM 2019 Challenge on Image Demoireing: Dataset and Study

no code implementations6 Nov 2019 Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis

In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.

Image Manipulation

A continual learning survey: Defying forgetting in classification tasks

1 code implementation18 Sep 2019 Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.

Classification Continual Learning +2

Learning to Exploit Stability for 3D Scene Parsing

no code implementations NeurIPS 2018 Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu

We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.

Scene Understanding

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 Few-shot Regression

Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images

1 code implementation12 May 2018 Grigorios Kalliatakis, Shoaib Ehsan, Ales Leonardis, Klaus McDonald-Maier

With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.

Representation Learning Transfer Learning

Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?

no code implementations9 Nov 2017 Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.

General Classification Material Classification

Rolling Shutter Correction in Manhattan World

no code implementations ICCV 2017 Pulak Purkait, Christopher Zach, Ales Leonardis

A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image.

Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database

no code implementations24 Sep 2017 Bruno Ferrarini, Shoaib Ehsan, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.

Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?

no code implementations12 Mar 2017 Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier

We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.

Semantic tracking: Single-target tracking with inter-supervised convolutional networks

no code implementations19 Nov 2016 Jingjing Xiao, Qiang Lan, Linbo Qiao, Ales Leonardis

Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking.

General Classification

A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors

no code implementations19 May 2016 Shoaib Ehsan, Adrian F. Clark, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier

Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed.

Automatic Selection of the Optimal Local Feature Detector

no code implementations19 May 2016 Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Ales Leonardis, Klaus D. McDonald-Maier

The efficiency and the good accuracy in determining the optimal feature detector for any operating condition, make the proposed tool suitable to be utilized in real visual applications.

Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-Manifold Shapes

no code implementations ICCV 2015 Mete Ozay, Umit Rusen Aktas, Jeremy L. Wyatt, Ales Leonardis

We represent the topological relationship between shape components using graphs, which are aggregated to construct a hierarchical graph structure for the shape vocabulary.

General Classification

A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization

no code implementations4 Mar 2015 Mete Ozay, Krzysztof Walas, Ales Leonardis

We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP).

Distributed Optimization Pose Estimation

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

1 code implementation21 Jan 2015 Umit Rusen Aktas, Mete Ozay, Ales Leonardis, Jeremy L. Wyatt

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP).

Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation

no code implementations23 Aug 2014 Sanja Fidler, Marko Boben, Ales Leonardis

At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects.

Evaluating multi-class learning strategies in a generative hierarchical framework for object detection

no code implementations NeurIPS 2009 Sanja Fidler, Marko Boben, Ales Leonardis

We explore and compare their computational behavior (space and time) and detection performance as a function of the number of learned classes on several recognition data sets.

Object Detection

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