DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
Adaptformer: Adapting vision transformers for scalable visual recogni- tion
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
BGG adapts vision foundation models using multi-granularity dilated convolutions and frequency-domain patch aggregation to achieve state-of-the-art cross-view geo-localization on University-1652 and SUES-200 with low training cost.
citing papers explorer
-
Deep Reprogramming Distillation for Medical Foundation Models
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
-
BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
BGG adapts vision foundation models using multi-granularity dilated convolutions and frequency-domain patch aggregation to achieve state-of-the-art cross-view geo-localization on University-1652 and SUES-200 with low training cost.