AnyUp: Universal Feature Upsampling
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911450498334720 |
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| author | Wimmer, Thomas Truong, Prune Rakotosaona, Marie-Julie Oechsle, Michael Tombari, Federico Schiele, Bernt Lenssen, Jan Eric |
| author_facet | Wimmer, Thomas Truong, Prune Rakotosaona, Marie-Julie Oechsle, Michael Tombari, Federico Schiele, Bernt Lenssen, Jan Eric |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnyUp: Universal Feature Upsampling Wimmer, Thomas Truong, Prune Rakotosaona, Marie-Julie Oechsle, Michael Tombari, Federico Schiele, Bernt Lenssen, Jan Eric Computer Vision and Pattern Recognition Machine Learning 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. |
| title | AnyUp: Universal Feature Upsampling |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2510.12764 |