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 …

With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

In recent years, remote sensing recognition technology has found extensive applications in
diverse fields, such as modern agriculture, forest management, urban planning, natural …

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 …

Due to the complexity of real-world applications, open set recognition is often more practical
than closed set recognition. Compared with closed set recognition, open set recognition …

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data,
which emerges unknown classes sequentially. Different from traditional closed set learning …

Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data
from activity classes that are not represented in the training data of a Machine Learning (ML) …

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen
significant advancements. Despite these advancements, the majority of research still …

Classifiers based on machine learning are usually trained to distinguish between several
known classes. For an electronic intelligence application, however, it is of great importance …