Bias Mitigating Few-Shot Class-Incremental Learning
Li-Jun Zhao Zhen-Duo Chen Zi-Chao Zhang Xin Luo Xin-Shun Xu
Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes
continually with limited novel class samples. A mainstream baseline for FSCIL
is first to train the whole model in the base session, then freeze the feature
extractor in the incremental sessions. Despite achieving high overall accuracy,
most methods exhibit notably low accuracy for incremental classes. Some recent
methods somewhat alleviate the accuracy imbalance between base and incremental
classes by fine-tuning the feature extractor in the incremental sessions, but
they further cause the accuracy imbalance between past and current incremental
classes. In this paper, we study the causes of such classification accuracy
imbalance for FSCIL, and abstract them into a unified model bias problem. Based
on the analyses, we propose a novel method to mitigate model bias of the FSCIL
problem during training and inference processes, which includes mapping ability
stimulation, separately dual-feature classification, and self-optimizing
classifiers. Extensive experiments on three widely-used FSCIL benchmark
datasets show that our method significantly mitigates the model bias problem
and achieves state-of-the-art performance.