NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

Loick , Paul Couairon, Eloi Zablocki, Alexandre Boulch, Nicolas Thome, Matthieu Cord

NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering: 1 upvotes on Hugging Face Daily Papers, #14 of 20 papers on 2025-11-27. Day-by-day upvote history.

Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off: classical filters are fast and broadly applicable but rely on fixed forms, while modern upsamplers achieve superior accuracy through learnable, VFM-specific forms at the cost of retraining for each VFM. We introduce Neighborhood Attention Filtering (NAF), which bridges this gap by learning adaptive spatial-and-content weights through Cross-Scale Neighborhood Attention and Rotary Position Embeddings (RoPE), guided solely by the high-resolution input image. NAF operates zero-shot: it upsamples features from any VFM without retraining, making it the first VFM-agnostic architecture to outperform VFM-specific upsamplers and achieve state-of-the-art performance across multiple downstream tasks. It maintains high efficiency, scaling to 2K feature maps and reconstructing intermediate-resolution maps at 18 FPS. Beyond feature upsampling, NAF demonstrates strong performance on image restoration, highlighting its versatility. Code and checkpoints are available at https://github.com/valeoai/NAF.

Paper page on Hugging Face · arXiv

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