Continual Visual Learning under Evolving Semantic Concept Shift
Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual systems, however, taxonomies, policies, and concept definitions can themselves evolve, causing the same visual evidence to require a different interpretation. We study this setting as evolving semantic concept shift and introduce SemReWrite, a framework for selectively updating obsolete visual--semantic mappings while preserving knowledge that remains valid. SemReWrite represents changes between old and revised semantic specifications, combines semantic discrepancy with sparse revised superv...
| Authors | Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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