Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Run He, Jiao Chen, Di Fang, Ming Li
| Summary:
Class-incremental learning (CIL) enables models to learn new classes
continually without forgetting previously acquired knowledge. Multi-label CIL
(MLCIL) extends CIL to a real-world scenario where each sample may belong to
multiple classes, introducing several challenges: label absence, which leads to
incomplete historical information due to missing labels, and class imbalance,
which results in the model bias toward majority classes. To address these
challenges, we propose Label-Augmented Analytic Adaptation (L3A), an
exemplar-free approach without storing past samples. L3A integrates two key
modules. The pseudo-label (PL) module implements label augmentation by
generating pseudo-labels for current phase samples, addressing the label
absence problem. The weighted analytic classifier (WAC) derives a closed-form
solution for neural networks. It introduces sample-specific weights to
adaptively balance the class contribution and mitigate class imbalance.
Experiments on MS-COCO and PASCAL VOC datasets demonstrate that L3A outperforms
existing methods in MLCIL tasks. Our code is available at
https://github.com/scut-zx/L3A.
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