PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
Unifying the complementary strengths of diverse Vision Foundation Models (VFMs) into a single efficient model is highly desirable but challenged by the negative transfer inherent in monolithic distillation. To address these feature conflicts, we introduce \textbf{PRISM}, a novel dual-stream Mixture-of-Experts (MoE) framework that synergizes VFMs via modular specialization. We propose a two-stage paradigm: (1) expertise deconstruction, where a teacher-conditional router guides experts to specialize in distinct representational subspaces to mitigate interference, followed by (2) dynamic recomposition, where the router learns to assemble these experts into tailored computational pathways for...
| Authors | Ying Tang, Dong Li, Youjia Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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