analiza aiqbench
PRISM introduces a dual-stream Mixture-of-Experts framework that resolves negative transfer issues in monolithic distillation by enabling modular specialization among diverse Vision Foundation Models. By deconstructing expertise into distinct subspaces and dynamically recomposing them via a teacher-conditional router, the method achieves state-of-the-art results on benchmark datasets like PASCAL-Context and NYUD-v2. This approach validates sparse, emergent specialization as a scalable strategy for integrating complementary visual knowledge without feature conflict.
PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
arXiv:2606.03444v1 Announce Type: cross Abstract: 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 downstream tasks. Experiments on PASCAL-Context and NYUD-v2 show that \textbf{PRISM} establishes a new state of the art, validating that sparse, emergent specialization is a scalable approach for integrating diverse visual knowledge.
Abstract: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 downstream tasks. Experiments on PASCAL-Context and NYUD-v2 show that \textbf{PRISM} establishes a new state of the art, validating that sparse, emergent specialization is a scalable approach for integrating diverse visual knowledge.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2606.03444 [cs.CV]
(or arXiv:2606.03444v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.03444 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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