Papers › JAFAR: Jack up Any Feature at Any Resolution
JAFAR: Jack up Any Feature at Any Resolution
Paul Couairon, Loick Chambon, Louis Serrano, Jean-Emmanuel Haugeard, Matthieu Cord, 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
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Feature Upsampling | ImageNet | JAFAR | ADCC | 73.3 | #1 of 8 | Archive leaderboard | report |
| Feature Upsampling | ImageNet | JAFAR | Average Drop | 17.4 | #1 of 8 | Archive leaderboard | report |
| Feature Upsampling | ImageNet | JAFAR | Average Increase | 30.9 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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