13 citations · 54 across the 21 of their papers we have counts for
35 papers
Instance-Level Composed Image Retrieval
Bill Psomas, George Retsinas, Nikos Efthymiadis +5
The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of h…
Composed Image Retrieval for Remote Sensing
Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis +4
This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descr…
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, Bingyi Cao +7
Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractica…
Object-Guided Day-Night Visual Localization in Urban Scenes
Assia Benbihi, Cédric Pradalier, Ondřej Chum
We introduce Object-Guided Localization (OGuL) based on a novel method of local-feature matching. Direct matching of local features is sensitive to significant changes in illuminat…
Results and findings of the 2021 Image Similarity Challenge
Zoë Papakipos, Giorgos Tolias, Tomas Jenicek +11
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods. There were 200 participants to the competition…
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
Nikos Efthymiadis, Giorgos Tolias, Ondrej Chum
This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far prec…
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…
Understanding and Improving Kernel Local Descriptors
Arun Mukundan, Giorgos Tolias, Andrei Bursuc +2
We propose a multiple-kernel local-patch descriptor based on efficient match kernels from pixel gradients. It combines two parametrizations of gradient position and direction, each…
Local Orthogonal-Group Testing
Ahmet Iscen, Ondrej Chum
This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval. Within the group testing framework we propose an efficient off-line co…
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
Ahmet Iscen, Yannis Avrithis, Giorgos Tolias +2
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. The two most successful…
Rectification from Radially-Distorted Scales
James Pritts, Zuzana Kukelova, Viktor Larsson +1
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The…
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Filip Radenović, Ahmet Iscen, Giorgos Tolias +2
In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset…
Mining on Manifolds: Metric Learning without Labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis +1
In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application a…
Coplanar Repeats by Energy Minimization
James Pritts, Denys Rozumnyi, M. Pawan Kumar +1
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization. The proposed energy functional combine…
Radially-Distorted Conjugate Translations
James Pritts, Zuzana Kukelova, Viktor Larsson +1
This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moder…
Fine-tuning CNN Image Retrieval with No Human Annotation
Filip Radenović, Giorgos Tolias, Ondřej Chum
Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representat…