JAFAR: Jack up Any Feature at Any Resolution

Paul Couairon, Loick , Louis Serrano, Jean-Emmanuel Haugeard, Matthieu Cord, Nicolas Thomé

JAFAR: Jack up Any Feature at Any Resolution: 10 upvotes on Hugging Face Daily Papers, #10 of 27 papers on 2025-06-16. Day-by-day upvote history.

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

Paper page on Hugging Face · arXiv

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