We introduce an approach to learn discriminative visual representations while exploiting
external semantic knowledge about object category relationships. Given a hierarchical …

In multi-task learning, multiple interrelated tasks are jointly learned to achieve better
performance. In many cases, if we can identify which tasks are related, we can also clearly …

Metric learning has become a very active research field. The most popular representative--
Mahalanobis metric learning--can be seen as learning a linear transformation and then …

The modern image search system requires semantic understanding of image, and a key yet
under-addressed problem is to learn a good metric for measuring the similarity between …

Learning a distance metric from the given training samples plays a crucial role in many
machine learning tasks, and various models and optimization algorithms have been …

Deep Metric Learning (DML) plays a critical role in various machine learning tasks.
However, most existing deep metric learning methods with binary similarity are sensitive to …

Metric learning learns a metric function from training data to calculate the similarity or
distance between samples. From the perspective of feature learning, metric learning …

In this paper, we devise a kernel version of the recently introduced keep it simple and
straightforward metric learning method, hence adding a novel dimension to its applicability …

We propose a new approach for metric learning by framing it as learning a sparse
combination of locally discriminative metrics that are inexpensive to generate from the …

Abstract Distance Metric Learning aims to learn an appropriate metric that faithfully
measures the distance between two data points. Traditional metric learning methods usually …