JAFAR: Jack up Any Feature at Any Resolution
Authors: name: Paul Couairon, name: Loick Chambon, name: Louis Serrano, name: Jean-Emmanuel Haugeard, name: Matthieu Cord, name: Nicolas Thome

Foundation Vision Encoders have become essential for a wide range of dense
vision tasks. However, their low-resolution spatial feature outputs necessitate
feature upsampling to produce the high-resolution modalities required for
downstream tasks. In this work, we introduce JAFAR, a lightweight and flexible
feature upsampler that enhances the spatial resolution of visual features from
any Foundation Vision Encoder to an arbitrary target resolution. JAFAR employs
an attention-based module designed to promote semantic alignment between
high-resolution queries, derived from low-level image features, and
semantically enriched low-resolution keys, using Spatial Feature Transform
(SFT) modulation. Notably, despite the absence of high-resolution supervision,
we demonstrate that learning at low upsampling ratios and resolutions
generalizes remarkably well to significantly higher output scales. Extensive
experiments show that JAFAR effectively recovers fine-grained spatial details
and consistently outperforms existing feature upsampling methods across a
diverse set of downstream tasks. Project page at
https://jafar-upsampler.github.io