In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.
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.
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.
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features.
Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds.
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.
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.
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile.
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
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.
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval.
This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns.