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UPLiFT-upsampler/
uplift_dinov2-s14

Name: uplift_dinov2-s14
Availability: InStock

pytorch

image-feature-extraction

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UPLiFT for DINOv2-S/14

Input Image	Base DINOv2 Features	UPLiFT Upsampled Features
Input	Base Features	UPLiFT Features

This is the official pretrained UPLiFT (Efficient Pixel-Dense Feature Upsampling with Local Attenders) model for the DINOv2-S/14 backbone.

UPLiFT is a lightweight method to upscale features from pretrained vision backbones to create pixel-dense feature maps. It uses Local Attenders to efficiently upsample low-resolution backbone features while preserving semantic information.

Model Details

Property	Value
Backbone	DINOv2-S/14 (`vit_small_patch14_dinov2.lvd142m`)
Backbone Channels	384
Patch Size	14
Upsampling Factor	2x per iteration
Local Attender Size	N=17
Training Dataset	ImageNet
Training Image Size	448x448
License	MIT

Installation

pip install 'uplift[vit] @ git+https://github.com/mwalmer-umd/UPLiFT.git'

Quick Start

python

import torch
from PIL import Image

# Load model (weights auto-download from HuggingFace)
model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov2_s14')

# Run inference
image = Image.open('your_image.jpg')
features = model(image)  # Returns pixel-dense features

Usage Options

Adjust Upsampling Iterations

Control the number of iterative upsampling steps (default: 4):

python

# Fewer iterations = lower memory usage
model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov2_s14', iters=4)

Raw UPLiFT Model (Without Backbone)

Load only the UPLiFT upsampling module without the DINOv2 backbone:

python

model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov2_s14',
                       include_extractor=False)

Return Base Features

Get both upsampled and original backbone features:

python

model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov2_s14',
                       return_base_feat=True)
upsampled_features, base_features = model(image)

Architecture

UPLiFT consists of:

Encoder: Processes the input image with a series of convolutional blocks to create dense representations to guide feature upsampling
Decoder: Upsamples features using transposed convolutions with bilinear residual connections
Local Attender: A local-neighborhood-based attention pooling module that maintains semantic consistency with the original features

The model uses encoder sharing, meaning a single encoder pass is used across all upsampling iterations for efficiency.

Intended Use

This model is designed for:

Creating pixel-dense feature maps from DINOv2 features
Dense prediction tasks (semantic segmentation, depth estimation, etc.)
Feature visualization and analysis
Research on vision foundation models

Limitations

Optimized specifically for DINOv2-S/14 features; may not generalize to other backbones without retraining
Performance depends on the quality of the underlying DINOv2 features
Higher iteration counts increase computation time
DINOv2 uses a patch size of 14, so 14x upsampling is required to make pixel-dense features. UPLiFT with 4 iterations performs 16x upsampling, slightly over-sampling the features. If exactly pixel-dense features are required, we recommend downsampling these over-sampled features to the correct size with bilinear or bicubic interpolation.

Citation

If you use UPLiFT in your research, please cite our paper.

@article{walmer2026uplift,
  title={UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders},
  author={Walmer, Matthew and Suri, Saksham and Aggarwal, Anirud and Shrivastava, Abhinav},
  journal={arXiv preprint arXiv:2601.17950},
  year={2026}
}

Acknowledgements

This work builds upon:

DINOv2 by Meta AI
timm for model loading

UPLiFT-upsampler/uplift_dinov2-s14

UPLiFT for DINOv2-S/14

Model Details

Links

Installation

Quick Start

Usage Options

Adjust Upsampling Iterations

Raw UPLiFT Model (Without Backbone)

Return Base Features

Architecture

Intended Use

Limitations

Citation

Acknowledgements

UPLiFT-upsampler/
uplift_dinov2-s14