15 citations · 18 across the 13 of their papers we have counts for
8 papers · 1 filter
Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis
Shufan Li, Greg Heinrich, Hanrong Ye +4
We propose Nemotron-Labs-Diffusion-Image, a state-of-the-art masked discrete diffusion model (MDM) for high-resolution text-to-image synthesis. Compared with prior work on masked i…
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…
BOP-ASK: Object-Interaction Reasoning for Vision-Language Models
Vineet Bhat, Sungsu Kim, Valts Blukis +6
Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interacti…
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…