The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …

Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

Optimizing a deep neural network is a fundamental task in computer vision, yet direct
training methods often suffer from over-fitting. Teacher-student optimization aims at …

In image classification, visual separability between different object categories is highly
uneven, and some categories are more difficult to distinguish than others. Such difficult …

Deep neural networks have improved image classification dramatically over the past
decade, but have done so by focusing on performance measures that treat all classes other …

This paper addresses the task of zero-shot image classification. The key contribution of the
proposed approach is to control the semantic embedding of images–one of the main …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world at multiple levels. However …

Convolutional Neural Network (CNN) image classifiers are traditionally designed to have
sequential convolutional layers with a single output layer. This is based on the assumption …