JAFAR: Jack up Any Feature at Any Resolution
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
685003c61d9b438aa3957adb
Paul Couairon
admin_assigned
2025-06-16T12:57:21.672Z
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Paul Couairon
user
paulcouairon
685003c61d9b438aa3957adc
Loick Chambon
685003c61d9b438aa3957add
Louis Serrano
admin_assigned
2025-06-16T12:57:35.319Z
6565d4d4d4ef4fe85fb50388
/avatars/7b8752b30aac8346ade2165a0ba09ca2.svg
Louis Serrano
user
sogeeking
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Jean-Emmanuel Haugeard
685003c61d9b438aa3957adf
Matthieu Cord
685003c61d9b438aa3957ae0
Nicolas Thome
admin_assigned
2025-06-16T12:57:55.446Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/lY9SbMEgkmo8TQ_SNneol.png
Nicolas Thomé
user
nicolasthome
JAFAR: Jack up Any Feature at Any Resolution
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
JAFAR is a lightweight feature upsampler using an attention-based module with Spatial Feature Transform modulation, enabling high-resolution features from Foundation Vision Encoders without high-resolution supervision.
Foundation Vision Encoders feature upsampling attention-based module Spatial Feature Transform SFT modulation high-resolution queries low-resolution keys semantic alignment fine-grained spatial details