Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
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DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmark across 10 modalities and 26 downstream tasks.
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From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
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Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmark across 10 modalities and 26 downstream tasks.