Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning

TL;DR

This survey reviews recent advances in few-shot class incremental learning, emphasizing challenges, new paradigms, and evaluation metrics to address data scarcity in continual learning environments.

Contribution

It provides a comprehensive overview of FSCIL, including formal objectives, prototype rectification, pre-trained models, language-guided mechanisms, and practical applications, highlighting open challenges and future directions.

Findings

FSCIL approaches improve learning with limited data.

Prototype rectification enhances class representations.

Pre-trained models and language guidance are promising new paradigms.

Abstract

Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest progress of few-shot class incremental learning (FSCIL) methods and related studies show insightful knowledge on how to tackle the problem. This paper presents a comprehensive survey on FSCIL that highlights several important aspects i.e. comprehensive and formal objectives of FSCIL approaches, the importance of prototype rectifications, the new learning paradigms based on pre-trained model and language-guided mechanism, the deeper analysis of FSCIL performance metrics and evaluation, and the practical contexts of FSCIL in various areas. Our extensive discussion presents the open challenges, potential solutions, and future directions of FSCIL.