@online{Wimmer2510.12764,
TITLE = {{AnyUp}: Universal Feature Upsampling},
AUTHOR = {Wimmer, Thomas and Truong, Prune and Rakotosaona, Marie-Julie and Oechsle, Michael and Tombari, Federico and Schiele, Bernt and Lenssen, Jan Eric},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2510.12764},
EPRINT = {2510.12764},
EPRINTTYPE = {arXiv},
YEAR = {2025},
MARGINALMARK = {$\bullet$},
ABSTRACT = {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.},
}