README.md

metadata

title: NAF Zero-Shot Feature Upsampling
emoji: 🎯
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.0.1
app_file: app.py
pinned: false
license: apache-2.0

🎯 NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

This Space demonstrates NAF (Neighborhood Attention Filtering), a method for upsampling features from Vision Foundation Models to any resolution without model-specific training.

🚀 Features

• Universal Upsampling: Works with any Vision Foundation Model (DINOv2, DINOv3, RADIO, DINO, SigLIP, etc.)
• Arbitrary Resolutions: Upsample features to any target resolution while maintaining aspect ratio
• Zero-Shot: No model-specific training or fine-tuning required
• Interactive Demo: Upload your own images or try sample images from various domains

🎨 How to Use

1. Upload an Image: Click "Upload Your Image" or select from sample images
2. Choose a Model: Select a Vision Foundation Model from the dropdown
3. Set Resolution: Choose the target resolution for upsampled features (64-512)
4. Click "Upsample Features": See the comparison between low and high-resolution features

📊 Visualization

The output shows three panels:

• Left: Your input image
• Center: Low-resolution features from the backbone (PCA visualization)
• Right: High-resolution features upsampled by NAF

Features are visualized using PCA for the first 3 principal components as RGB channels.

🔬 Supported Models

• DINOv3: Latest self-supervised vision models
• RADIO v2.5: High-performance vision backbones
• DINOv2: Self-supervised learning with registers
• DINO: Original self-supervised ViT
• SigLIP: Contrastive vision-language models

📖 Learn More

• Paper: NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering
• Code: GitHub Repository
• Organization: Valeo.ai

💡 Use Cases

NAF enables better feature representations for:

• Dense prediction tasks (segmentation, depth estimation)
• High-resolution visual understanding
• Feature matching and correspondence
• Vision-language alignment

⚙️ Technical Details

• Input: Images up to 512px (maintains aspect ratio)
• Processing: Backbone feature extraction → NAF upsampling
• Output: High-resolution features at target resolution
• Device: Runs on CPU (free tier) or GPU (faster inference)

🤝 Citation

If you use NAF in your research, please cite:

@article{chambon2025naf,
  title={NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering},
  author={Chambon, Lucas and others},
  journal={arXiv preprint arXiv:2501.01535},
  year={2025}
}

📜 License

This demo is released under the Apache 2.0 license.