3 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
RADIO1D: Elastic Representations for Condensed Vision Modeling
Greg Heinrich, Mike Ranzinger, Collin McCarthy +6
This paper challenges the assumption that vision-language models (VLMs) require fixed patch-based 2D vision features. Analyzing fine-tuned vision encoders, we find that representat…
C-RADIOv4 (Tech Report)
Mike Ranzinger, Greg Heinrich, Collin McCarthy +4
By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. I…
FeatSharp: Your Vision Model Features, Sharper
Mike Ranzinger, Greg Heinrich, Pavlo Molchanov +3
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 percepti…
RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models
Greg Heinrich, Mike Ranzinger, Hongxu +6
Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DIN…
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
Mike Ranzinger, Greg Heinrich, Jan Kautz +1
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,…
ViR: Towards Efficient Vision Retention Backbones
Ali Hatamizadeh, Michael Ranzinger, Shiyi Lan +3
Vision Transformers (ViTs) have attracted a lot of popularity in recent years, due to their exceptional capabilities in modeling long-range spatial dependencies and scalability for…