@inproceedings{
huang2024class,
title={Class Balance Matters to Active Class-Incremental Learning},
author={Zitong Huang and Ze Chen and Yuanze Li and Bowen Dong and Erjin Zhou and Yong Liu and Rick Siow Mong Goh and Chun-Mei Feng and Wangmeng Zuo},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=0KrM2KwV7v}
}
Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may not always cover the most informative samples, which largely restricts the capability of incremental learner. We aim to start from a pool of large-scale unlabeled data and then annotate the most informative samples for incremental learning. Based on this purpose, this paper introduces the Active Class-Incremental Learning (ACIL).  The objective of ACIL is to select the most informative samples from the unlabeled pool to effectively train an incremental learner, aiming to maximize the performance of the resulting model. Note that vanilla active learning algorithms suffer from class-imbalanced distribution among annotated samples, which restricts the ability of incremental learning. To achieve both class balance and informativeness in chosen samples, we propose $\textbf{C}$lass-$\textbf{B}$alanced $\textbf{S}$election ($\textbf{CBS}$) strategy. Specifically, we first cluster the features of all unlabeled images into multiple groups.  Then for each cluster, we employ greedy selection strategy to ensure that the Gaussian distribution of the sampled features closely matches the Gaussian distribution of all unlabeled features within the cluster. Our CBS can be plugged and played into those CIL methods which are based on pretrained models with prompts tunning technique. Extensive experiments under ACIL protocol across five diverse datasets demonstrate that CBS outperforms both random selection and other SOTA active learning approaches.
Class Balance Matters to Active Class-Incremental Learning
Zitong Huang Ze Chen Yuanze Li Bowen Dong Erjin Zhou Yong Liu Rick Siow Mong Goh Chun-Mei Feng Wangmeng Zuo