Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent
years for its pivotal role in addressing continuously arriving classes. However, it encounters …

Abstract Existing approaches to Domain Incremental Learning (DIL) address catastrophic
forgetting by storing and rehearsing exemplars from old domains. However, exemplar-based …

Few-Shot Class-Incremental Learning (FSCIL) faces a huge stability-plasticity challenge due
to continuously learning knowledge from new classes with a small number of training …

In dynamic real-world scenarios, continuous learning without forgetting old knowledge is
essential, particularly in environments with stricter privacy protection or resource …

Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited
samples in a few-shot fashion. Inherited from the classical class-incremental learning …

Current state-of-the-art artificial intelligence (AI) struggles with accurate interpretation of out-
of-library objects. One method proposed remedy is analogical reasoning (AR), which utilizes …

Class incremental learning has made great progress in solving the problem of catastrophic
forgetting through knowledge distillation method and sample playback method. However …

Online task-free continual learning (OTFCL) is a more challenging variant of continual
learning that emphasizes the gradual shift of task boundaries and learning in an online …

Abstract Generalized Few-Shot Learning (GFSL) aims to recognize novel classes with
limited training samples without forgetting knowledge of auxiliary data (base classes). Most …

Few-shot Class-Incremental Learning (FSCIL) addresses the challenges of evolving data
distributions and the difficulty of data acquisition in real-world scenarios. To counteract the …