# AnyUp: Universal Feature Upsampling

arXiv: https://arxiv.org/abs/2510.12764 · HTML: https://mlsanity.com/paper/2510.12764/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 43 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [Probing the 3D Awareness of Visual Foundation Models](https://mlsanity.com/paper/2404.08636/) (2024)
- [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://mlsanity.com/paper/2312.06709/) (2023)
- [StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal](https://mlsanity.com/paper/2406.16864/) (2024)
- [CARAFE: Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/1905.02188/) (2019)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation](https://mlsanity.com/paper/2312.12359/) (2023)
- [MEt3R: Measuring Multi-View Consistency in Generated Images](https://mlsanity.com/paper/2501.06336/) (2025)
- [OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views](https://mlsanity.com/paper/2404.03650/) (2024)
- [High Fidelity Visualization of What Your Self-Supervised Representation Knows About](https://mlsanity.com/paper/2112.09164/) (2021)
- [Learning to Upsample by Learning to Sample](https://mlsanity.com/paper/2308.15085/) (2023)
- [Denoising Vision Transformers](https://mlsanity.com/paper/2401.02957/) (2024)
- [SAPA: Similarity-Aware Point Affiliation for Feature Upsampling](https://mlsanity.com/paper/2209.12866/) (2022)
- [LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors](https://mlsanity.com/paper/2403.14625/) (2024)
- [LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models](https://mlsanity.com/paper/2504.14032/) (2025)
- [JAFAR: Jack up Any Feature at Any Resolution](https://mlsanity.com/paper/2506.11136/) (2025)

## Built on by (most cited first, 28 of 28)

- [C3G: Learning Compact 3D Representations with 2K Gaussians](https://mlsanity.com/paper/2512.04021/) (2025)
- [INSID3: Training-Free In-Context Segmentation with DINOv3](https://mlsanity.com/paper/2603.28480/) (2026)
- [Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling](https://mlsanity.com/paper/2511.16301/) (2025)
- [NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering](https://mlsanity.com/paper/2511.18452/) (2025)
- [Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving](https://mlsanity.com/paper/2602.06159/) (2026)
- [HD-VGGT: High-Resolution Visual Geometry Transformer](https://mlsanity.com/paper/2603.27222/) (2026)
- [FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute](https://mlsanity.com/paper/2609.08848/) (2026)
- [UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders](https://mlsanity.com/paper/2601.17950/) (2026)
- [FaCT: Faithful Concept Traces for Explaining Neural Network Decisions](https://mlsanity.com/paper/2510.25512/) (2025)
- [Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers](https://mlsanity.com/paper/2603.02919/) (2026)
- [Falcon Perception](https://mlsanity.com/paper/2603.27365/) (2026)
- [LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2604.01388/) (2026)
- [DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/2605.03175/) (2026)
- [CountZES: Counting via Zero-Shot Exemplar Selection](https://mlsanity.com/paper/2512.16415/) (2025)
- [Geometry without Position? When Positional Embeddings Help and Hurt Spatial Reasoning](https://mlsanity.com/paper/2601.22231/) (2026)
- [Spectral Probing of Feature Upsamplers in 2D-to-3D Scene Reconstruction](https://mlsanity.com/paper/2603.05787/) (2026)
- [DiveUp: Learning Feature Upsampling from Diverse Vision Foundation Models](https://mlsanity.com/paper/2603.13571/) (2026)
- [dinov3.seg: Open-Vocabulary Semantic Segmentation with DINOv3](https://mlsanity.com/paper/2603.19531/) (2026)
- [SPAR: Single-Pass Any-Resolution ViT for Open-vocabulary Segmentation](https://mlsanity.com/paper/2604.02252/) (2026)
- [MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models](https://mlsanity.com/paper/2604.14582/) (2026)
- [Frozen Vision Transformers for Dense Prediction on Small Datasets: A Case Study in Arrow Localization](https://mlsanity.com/paper/2604.16758/) (2026)
- [Weighted Reverse Convolution for Feature Upsampling](https://mlsanity.com/paper/2605.17472/) (2026)
- [ViT-Up: Faithful Feature Upsampling for Vision Transformers](https://mlsanity.com/paper/2606.14024/) (2026)
- [UNIEGO: Proxies as Mediators for Unified Egocentric Video Representation Learning](https://mlsanity.com/paper/2606.20559/) (2026)
- [RaysUp: Ultra-light Universal Feature Upsampling via Geometry-Aware Ray Representation](https://mlsanity.com/paper/2606.22749/) (2026)
- [From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation](https://mlsanity.com/paper/2606.23486/) (2026)
- [ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals](https://mlsanity.com/paper/2609.37986/) (2026)
- [One Tile, Multiple Instances: Rethinking MIL for Sparse Diagnostic Evidence](https://mlsanity.com/paper/2610.04853/) (2026)

## Abstract (arXiv)

We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propose an inference-time feature-agnostic upsampling architecture to alleviate this limitation and improve upsampling quality. In our experiments, AnyUp sets a new state of the art for upsampled features, generalizes to different feature types, and preserves feature semantics while being efficient and easy to apply to a wide range of downstream tasks.