Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes
continually from limited samples without forgetting the old classes. The mainstream …

The emergence of COVID-19 has had a global and profound impact, not only on society as a
whole, but also on the lives of individuals. Various prevention measures were introduced …

Abstract Class-Incremental Learning (CIL) aims to build classification models from data
streams. At each step of the CIL process, new classes must be integrated into the model …

Abstract Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning
task, where limited training examples are available during several learning sessions. To …

Few-shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes
based on very limited training data without forgetting the old ones encountered. Existing …

Abstract Few-Shot Class-Incremental Learning (FSCIL) introduces a paradigm in which the
problem space expands with limited data. FSCIL methods inherently face the challenge of …

The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with
limited labeled samples and preserve previous capabilities simultaneously, while current …

Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in machine
learning, as it necessitates the continuous learning of new classes from sparse labeled …

Few-shot class-incremental learning (FSCIL) confronts the challenge of integrating new
classes into a model with minimal training samples while preserving the knowledge of …