XRFormer: Multiscale Tokenization for XRF Representation Learning
X-ray fluorescence (XRF) spectroscopy is a key modality for material analysis in cultural heritage. However, automated learning from XRF spectra remains challenging: XRF spectra are complex one-dimensional signals composed of sharp elemental peaks, broader structures, and background variations that are not taken into account by existing learning-based models. This paper introduces XRFormer, a transformer architecture tailored to XRF spectra through a multiscale convolutional tokenizer that injects locality and multi-resolution inductive biases before global self-attention. The tokenizer progressively reduces spectral resolution while increasing embedding dimensionality, and the resulting ...
| Authors | Sofiane Daimellah, Sylvie Le Hégarat-Mascle, Clotilde Boust |
|---|---|
| Primary category | cs.CV |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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