1 code implementation • 26 Feb 2022 • Nikos Efthymiadis, Giorgos Tolias, Ondrej Chum
To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.
no code implementations • 8 Feb 2022 • Zoë Papakipos, Giorgos Tolias, Tomas Jenicek, Ed Pizzi, Shuhei Yokoo, Wenhao Wang, Yifan Sun, Weipu Zhang, Yi Yang, Sanjay Addicam, Sergio Manuel Papadakis, Cristian Canton Ferrer, Ondrej Chum, Matthijs Douze
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
1 code implementation • ECCV 2020 • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum, Cordelia Schmid
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.
1 code implementation • 15 May 2019 • Oriane Siméoni, Yannis Avrithis, Ondrej Chum
We propose a novel method of deep spatial matching (DSM) for image retrieval.
no code implementations • CVPR 2019 • Arun Mukundan, Giorgos Tolias, Ondrej Chum
We evaluate the descriptor on standard benchmarks.
1 code implementation • CVPR 2019 • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum
In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network.
no code implementations • ECCV 2018 • Ahmet Iscen, Ondrej Chum
This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval.
no code implementations • 23 Jul 2018 • Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondrej Chum
State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line.
1 code implementation • 16 Jul 2018 • James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features.
1 code implementation • CVPR 2018 • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum
Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds.
1 code implementation • CVPR 2018 • James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion.
no code implementations • 26 Nov 2017 • James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization.
no code implementations • 14 Sep 2017 • Oriane Siméoni, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum
Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion.
no code implementations • 25 Jul 2017 • Arun Mukundan, Giorgos Tolias, Ondrej Chum
We propose a multiple-kernel local-patch descriptor based on efficient match kernels of patch gradients.
no code implementations • 21 Apr 2017 • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum
Location recognition is commonly treated as visual instance retrieval on "street view" imagery.
1 code implementation • CVPR 2018 • Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondrej Chum
This makes the Euclidean nearest neighbor search biased for this task.
3 code implementations • CVPR 2017 • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
1 code implementation • 24 Aug 2016 • Javier Aldana-Iuit, Dmytro Mishkin, Ondrej Chum, Jiri Matas
A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile.
no code implementations • 12 Jul 2016 • Martin Cadik, Jan Vasicek, Michal Hradis, Filip Radenovic, Ondrej Chum
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.
no code implementations • CVPR 2016 • Filip Radenovic, Johannes L. Schonberger, Dinghuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
no code implementations • ICCV 2015 • Ondrej Chum
Approximating non-linear kernels by finite-dimensional feature maps is a popular approach for speeding up training and evaluation of support vector machines or to encode information into efficient match kernels.
no code implementations • CVPR 2015 • Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, Jan-Michael Frahm
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
no code implementations • 13 Apr 2015 • Filip Radenovic, Herve Jegou, Ondrej Chum
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval.
no code implementations • CVPR 2014 • James Pritts, Ondrej Chum, Jiri Matas
This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns.