C-RADIOv4-H
Papers using or citing this model 5
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AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various...
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RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models
Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DINO, and SAM. This strategy enables the efficient creation...
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PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed "agglomerative models." We build upon this body of...
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C-RADIOv4 (Tech Report)
By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. In this tech report, we describe the most recent release...
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FeatSharp: Your Vision Model Features, Sharper
The feature maps of vision encoders are fundamental to myriad modern AI tasks, ranging from core perception algorithms (e.g. semantic segmentation, object detection, depth perception, etc.) to modern multimodal understanding in...