2025/06/10 by Paul Couairon, Loïck Chambon, Couairon, Paul +11 · 3 voices · 7 citations
Medicine · #Lung Cancer Treatments and Mutations #cs.CV #eess.IV
paper · pdf · doi:10.48550/arxiv.2506.11136
openalex publication_date 2025/06/10 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
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