FeatSharp: Your Vision Model Features, Sharper
Mike Ranzinger, Greg Heinrich, Pavlo Molchanov, Bryan Catanzaro, Andrew Tao
Abstract
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 vision-language models (VLMs). Currently, in computer vision, the frontier of general purpose vision backbones is Vision Transformers (ViT), typically trained using contrastive loss (e.g. CLIP). A key problem with most off-the-shelf ViTs, particularly CLIP, is that these models are inflexibly low resolution. Most run at $224 \times 224$px, while the "high-resolution" versions are around $378-448$px, but still inflexible. We introduce a novel method to coherently and cheaply upsample the feature maps of low-resolution vision encoders while picking up on fine-grained details that would otherwise be lost due to resolution. We demonstrate the effectiveness of this approach on core perception tasks as well as within agglomerative model training using RADIO as a way of providing richer targets for distillation. Code available at https://github.com/NVlabs/FeatSharp
BibTeX
@inproceedings{
ranzinger2025featsharp,
title={FeatSharp: Your Vision Model Features, Sharper},
author={Mike Ranzinger and Greg Heinrich and Pavlo Molchanov and Bryan Catanzaro and Andrew Tao},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=lioemOcq3H}
}