Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world scenarios such as autonomous driving, a system trained on urban roads in sunny weather may later need to operate in rural or highway environments with different traffic patterns and weather conditions. This requires the model not only to overcome catastrophic forgetting, but also to effectively handle domain shifts. In this paper, we propose CrOss-sample Relational Fusion (CORF), a unified framework to address domain shift and catastrophic forgetting simultaneously. To enhance generalizability, we perform selective refinement of training sam...
| Authors | Zhen-Hao Xie, Yan Wang, Hao Sun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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