PRISM is a two-stage MoE framework that achieves new state-of-the-art results on PASCAL-Context and NYUD-v2 by enabling self-organized expert specialization across diverse vision foundation models.
Adaptive multi-teacher multi-level knowledge distillation
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A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
citing papers explorer
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PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
PRISM is a two-stage MoE framework that achieves new state-of-the-art results on PASCAL-Context and NYUD-v2 by enabling self-organized expert specialization across diverse vision foundation models.
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Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.