… a dynamic match kernel by adaptively calculating the matching thresholds between query
… distance among deep CNN features. In contrast to the typical static match kernel which is …

… corresponding to matching based on deep convolutional features, … As expected, matches
produced by deep features have much … Efficient match kernel between sets of features for visual …

… as stereo-matching or contentbased image retrieval. We introduce a deep convolutional
architecture that … We introduce a feature representation for patches that is based on the kernel …

… between deep learning models and shallow kernel machines. … kernel machines can match
deep neural network models, for very similar generalization performance. Furthermore, kernel …

… However, it is impractical to use such kernels for large datasets due to their significant …
propose efficient match kernels (EMK) that map local features to a low dimensional feature space …

… In this paper, we adopt a deep Convolutional neural network (CNN) model, which attention
… patch, in image feature points matching. CNN obtains the feature by convolution kernel which …

… Over the past decades, growing amount and diversity of methods have been proposed
for image matching, particularly with the development of deep learning techniques over the …

… kernel selection method for mean embedding matching. DAN can learn transferable features
… one of the most critical contributors to the efficacy of deep learning, and we believe it is also …

… the local convolution kernel for each edge from a global collection of … matching model, and
for BCAGM, we use the same feature and similarity function as Section 4.1. For deep learning …

… from hand-crafted feature descriptors, eg SIFT (Scale Invariant Feature Transform), to machine
learning and deep learning based descriptors. The machine learning models, the training …