Rethinking Few-shot Class-incremental Learning: Learning from Yourself
This work addresses evaluation limitations in few-shot class-incremental learning, which is important for AI systems needing to learn new classes with limited data, though it is incremental as it builds on existing transformer-based methods.
The paper tackles the problem of few-shot class-incremental learning by proposing a new evaluation metric (gAcc) and a method that leverages intermediate features of vision transformers to improve novel-class performance, achieving state-of-the-art results on three datasets.
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 setting, the popular benchmark of FSCIL uses averaged accuracy (aAcc) and last-task averaged accuracy (lAcc) as the evaluation metrics. However, we reveal that such evaluation metrics may not provide adequate emphasis on the novel class performance, and the continual learning ability of FSCIL methods could be ignored under this benchmark. In this work, as a complement to existing metrics, we offer a new metric called generalized average accuracy (gAcc) which is designed to provide an extra equitable evaluation by incorporating different perspectives of the performance under the guidance of a parameter $α$. We also present an overall metric in the form of the area under the curve (AUC) along the $α$. Under the guidance of gAcc, we release the potential of intermediate features of the vision transformers to boost the novel-class performance. Taking information from intermediate layers which are less class-specific and more generalizable, we manage to rectify the final features, leading to a more generalizable transformer-based FSCIL framework. Without complex network designs or cumbersome training procedures, our method outperforms existing FSCIL methods at aAcc and gAcc on three datasets. See codes at https://github.com/iSEE-Laboratory/Revisting_FSCIL