Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

Hyperspectral imaging has been used in a variety of geological applications since its advent
in the 1970s. In the last few decades, different techniques have been developed by …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Person re-identification (ReID) is an important task in wide area video surveillance which
focuses on identifying people across different cameras. Recently, deep learning networks …

Existing zero-shot learning (ZSL) models typically learn a projection function from a feature
space to a semantic embedding space (eg attribute space). However, such a projection …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

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 …