README.md

---
license: cc-by-nc-sa-4.0
pipeline_tag: image-feature-extraction
---

# ViT-Up

**ViT-Up** is an implicit feature upsampler for Vision Transformers that predicts backbone-aligned features at arbitrary continuous image coordinates.

This repository provides pretrained ViT-Up weights for DINOv3-S+ and DINOv3-B.

- **Paper**: [ViT-Up: Faithful Feature Upsampling for Vision Transformers](https://huggingface.co/papers/2606.14024)
- **Project page**: https://vitup.papers.discuna.com/
- **Code**: https://github.com/krispinwandel/vit-up

## Sample Usage

ViT-Up models can be loaded directly with `torch.hub.load`. The Hub entry points download ViT-Up weights from Hugging Face and load the matching DINOv3 backbone.

```python
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

# Available entry points:
# - vit_up_dinov3_splus
# - vit_up_dinov3_base
model = torch.hub.load(
    "krispinwandel/vit-up",
    "vit_up_dinov3_splus",
    pretrained=True,
    trust_repo=True,
    device=device,
).eval()

images = torch.randn(1, 3, 448, 448, device=device)
query_coords = torch.rand(1, 100, 2, device=device)  # normalized (x, y) in [0, 1]

with torch.no_grad():
    features = model(images, query_coords)

print(features.shape)  # (B, N_queries, D)

# Alternative API
model.set_images(images)
features = []
query_chunk_size = 10
for i in range(0, query_coords.shape[1], query_chunk_size):
    chunk_coords = query_coords[:, i : i + query_chunk_size]
    chunk_features = model(query_coords=chunk_coords)
    features.append(chunk_features)
features = torch.cat(features, dim=1)
print(features.shape)  # (B, N_queries, D)
```

## Citation

```bibtex
@misc{wandel2026vitupfaithfulfeatureupsampling,
      title={ViT-Up: Faithful Feature Upsampling for Vision Transformers}, 
      author={Krispin Wandel and Jingchuan Wang and Hesheng Wang},
      year={2026},
      eprint={2606.14024},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.14024}, 
}
```