7 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…
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen, Zhiqi Li, Shihao Wang +16
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and h…
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Min Shi, Fuxiao Liu, Shihao Wang +13
The ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs). Recent work indicates that enhanced visual perception…
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…