Knowledge Adaptation Network for Few-Shot Class-Incremental Learning

TL;DR

This paper introduces KANet, a novel approach for few-shot class-incremental learning that leverages foundation models and a knowledge adapter to improve class representation and incremental learning performance.

Contribution

The paper proposes KANet, which uses CLIP as a backbone and introduces a Knowledge Adapter and Incremental Pseudo Episode Learning to enhance class representations and incremental learning.

Findings

Achieves competitive results on CIFAR100, CUB200, and ImageNet-R datasets.

Outperforms existing methods in few-shot class-incremental learning tasks.

Demonstrates the effectiveness of combining foundation models with knowledge adaptation.

Abstract

Few-shot class-incremental learning (FSCIL) aims to incrementally recognize new classes using a few samples while maintaining the performance on previously learned classes. One of the effective methods to solve this challenge is to construct prototypical evolution classifiers. Despite the advancement achieved by most existing methods, the classifier weights are simply initialized using mean features. Because representations for new classes are weak and biased, we argue such a strategy is suboptimal. In this paper, we tackle this issue from two aspects. Firstly, thanks to the development of foundation models, we employ a foundation model, the CLIP, as the network pedestal to provide a general representation for each class. Secondly, to generate a more reliable and comprehensive instance representation, we propose a Knowledge Adapter (KA) module that summarizes the data-specific knowledge from training data and fuses it into the general representation. Additionally, to tune the knowledge learned from the base classes to the upcoming classes, we propose a mechanism of Incremental Pseudo Episode Learning (IPEL) by simulating the actual FSCIL. Taken together, our proposed method, dubbed as Knowledge Adaptation Network (KANet), achieves competitive performance on a wide range of datasets, including CIFAR100, CUB200, and ImageNet-R.