Search Results for author:
Found 24 papers, 10 papers with code
Fast Spectral Ranking for Similarity Search
This makes the Euclidean nearest neighbor search biased for this task.
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
Label Propagation for Deep Semi-supervised Learning
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.
Mining on Manifolds: Metric Learning without Labels
Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds.
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
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.
Graph convolutional networks for learning with few clean and many noisy labels
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.
In the Saddle: Chasing Fast and Repeatable Features
A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile.
Radially-Distorted Conjugate Translations
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.
Rectification from Radially-Distorted Scales
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features.
Unsupervised object discovery for instance recognition
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.
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization.
Multiple-Kernel Local-Patch Descriptor
We propose a multiple-kernel local-patch descriptor based on efficient match kernels of patch gradients.
Panorama to panorama matching for location recognition
Location recognition is commonly treated as visual instance retrieval on "street view" imagery.
Camera Elevation Estimation from a Single Mountain Landscape Photograph
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
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.
Local Orthogonal-Group Testing
This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval.
Detection, Rectification and Segmentation of Coplanar Repeated Patterns
This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns.
From Single Image Query to Detailed 3D Reconstruction
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
From Dusk Till Dawn: Modeling in the Dark
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
Low Dimensional Explicit Feature Maps
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.
Explicit Spatial Encoding for Deep Local Descriptors
We evaluate the descriptor on standard benchmarks.
Local Features and Visual Words Emerge in Activations
We propose a novel method of deep spatial matching (DSM) for image retrieval.
Results and findings of the 2021 Image Similarity Challenge
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
