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Continual Visual Learning under Evolving Semantic Concept Shift

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

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

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-24T23:14:46.000Z

First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.