AnyUp: Universal Feature Upsampling

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 up-sampling 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. An important limitation of such pre-trained models, which are usually transformer-based, is that their output feature map resolution is limited to the number of transformer tokens, preventing the prediction of pixel-level features. Therefore, several recent works, such as FeatUp (Fu et al., 2024), LoftUp (Huang et al., 2025), or JAFAR (Couairon et al., 2025) propose learned feature upsampling methods. While such feature upsampling methods perform well when paired with the vision encoders with which they were trained, they are generally not encoder-agnostic at inference time and need to be retrained to be usable with a different feature extractor. This can be costly or, in the case of the latest large vision models (Sim eoni et al., 2025), even infeasible with limited computing resources, 1 AnyUp is the first learnable method that generalizes to any input feature at inference time, while being able to upsample from any to any resolution and being task-agnostic.