13 citations · 54 across the 21 of their papers we have counts for
9 papers · 1 filter
Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations
James Pritts, Zuzana Kukelova, Viktor Larsson +2
This paper introduces minimal solvers that jointly solve for radial lens undistortion and affine-rectification using local features extracted from the image of coplanar translated…
Graph convolutional networks for learning with few clean and many noisy labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis +2
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given. The structure of clean and noisy data is modeled by a g…
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower
Giorgos Tolias, Filip Radenovic, Ondřej Chum
Access to online visual search engines implies sharing of private user content - the query images. We introduce the concept of targeted mismatch attack for deep learning based retr…
No Fear of the Dark: Image Retrieval under Varying Illumination Conditions
Tomas Jenicek, Ondřej Chum
Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned. Prior to extracting image d…
Linking Art through Human Poses
Tomas Jenicek, Ondřej Chum
We address the discovery of composition transfer in artworks based on their visual content. Automated analysis of large art collections, which are growing as a result of art digiti…
Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales
James Pritts, Zuzana Kukelova, Viktor Larsson +2
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from the image of rigidly-transformed coplanar features. The solvers…
Local Features and Visual Words Emerge in Activations
Oriane Siméoni, Yannis Avrithis, Ondrej Chum
We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations…
Explicit Spatial Encoding for Deep Local Descriptors
Arun Mukundan, Giorgos Tolias, Ondrej Chum
We propose a kernelized deep local-patch descriptor based on efficient match kernels of neural network activations. Response of each receptive field is encoded together with its sp…
Label Propagation for Deep Semi-supervised Learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis +1
Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Clas…