Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …

Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …

Abstract Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and social …

Deep learning has achieved a remarkable performance breakthrough in several fields, most
notably in speech recognition, natural language processing, and computer vision. In …

Existing data-dependent hashing methods usually learn hash functions from pairwise or
triplet data relationships, which only capture the data similarity locally, and often suffer from …

A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …

Person re-identification (re-ID) has become increasingly popular in the community due to its
application and research significance. It aims at spotting a person of interest in other …