SPA unlocks patch-level features in CLIP for class-incremental learning via semantic-guided selection and optimal transport alignment with class descriptions, plus projectors and pseudo-feature replay to reduce forgetting.
Addressing imbalanced domain-incremental learning through dual-balance collaborative experts
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
E²-LoRA structures low-rank adaptations by energy concentration and ordering with dynamic rank allocation to achieve state-of-the-art continual learning.
citing papers explorer
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Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
SPA unlocks patch-level features in CLIP for class-incremental learning via semantic-guided selection and optimal transport alignment with class descriptions, plus projectors and pseudo-feature replay to reduce forgetting.
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Energy-Structured Low-Rank Adaptation for Continual Learning
E²-LoRA structures low-rank adaptations by energy concentration and ordering with dynamic rank allocation to achieve state-of-the-art continual learning.