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 …

We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-
Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by …

In this paper, we present a neural network based representation for the Open Set
Recognition problem. In this representation instances from the same class are close to each …

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 …

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 …

In open set recognition (OSR), almost all existing methods are designed specially for
recognizing individual instances, even these instances are collectively coming in batch …

In recent years, sensor-based human activity recognition (HAR) technology has been the
focus of extensive research and has been successfully applied to many aspects of people's …

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 …

Recent advancements in prototype-based few-shot open-set recognition (FSOSR)
approaches reject outliers based on the high metric distances from the known class …

Multi-modality pre-trained models (PTMs) have considerably boosted the performance on a
broad range of computer vision topics. Still, they have not been explored purposefully in …