In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

When the training and the test data belong to different domains, the accuracy of an object
classifier is significantly reduced. Therefore, several algorithms have been proposed in the …

Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

The perceived success of recent visual recognition approaches has largely been derived
from their performance on classification tasks, where all possible classes are known at …

Real-world tasks in computer vision often touch upon open set recognition: multi-class
recognition with incomplete knowledge of the world and many unknown inputs. Recent work …

As science attempts to close the gap between man and machine by building systems
capable of learning, we must embrace the importance of the unknown. The ability to …

Since annotating and curating large datasets is very expensive, there is a need to transfer
the knowledge from existing annotated datasets to unlabelled data. Data that is relevant for …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

Monitoring of postures and activities is used in many clinical and research applications,
some of which may require highly reliable posture and activity recognition with desired …

Open set recognition is a classification-like task. It is accomplished not only by the
identification of observations which belong to targeted classes (ie, the classes among those …