Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

Deep learning-based fire detection models are usually trained offline on static datasets. For
continuously increasing heterogeneous sensor data, incremental learning is a resolution to …

Deep neural networks for scene perception in automated vehicles achieve excellent results
for the domains they were trained on. However, in real-world conditions, the domain of …

Abstract Domain Incremental Learning (DIL) focuses on handling complex domain shifts of a
continuous data stream for visual tasks such as image classification and image …

In recent years, online distillation has emerged as a powerful technique for adapting real-
time deep neural networks on the fly using a slow, but accurate teacher model. However, a …

Deep learning models can achieve high accuracy when trained on large amounts of labeled
data. However, real-world scenarios often involve several challenges: Training data may …

Traditional change detectors are ill-equipped for incremental learning (IL). Existing IL
methods address the problem of catastrophic forgetting by artificially adding categories and …

Anomaly detection (AD) has gained widespread attention due to its ability to identify defects
in industrial scenarios using only normal samples. Although traditional AD methods …