XRFormer: Multiscale Tokenization for XRF Representation Learning

• URL: http://arxiv.org/abs/2607.06424v1
• Date: Tue, 07 Jul 2026 15:51:06 GMT
• Title: XRFormer: Multiscale Tokenization for XRF Representation Learning
• Abstract summary: X-ray fluorescence (XRF) spectroscopy is a key modality for material analysis in cultural heritage.<n>XRF spectra are complex one-dimensional signals composed of sharp elemental peaks, broader structures, and background variations.<n>This paper introduces XRFormer, a transformer architecture tailored to XRF spectra through a multiscale convolutional tokenizer.
• Score: 0.688204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 token sequence is processed by a standard transformer encoder. We further investigate self-supervised pretraining for XRF representation learning using Masked Spectral modeling (MSM) and a physics-informed Peak Presence Prediction (PPP) objective. Experiments on the Pigments Checker STANDARD v.5 dataset for pigment identification and unmixing show that XRFormer consistently outperforms ViT, SpectralFormer (with and without CAF), and a 1D-CNN baseline for pigment identification. For pigment unmixing, XRFormer achieves robust abundance estimation while maintaining significantly higher parameter efficiency than SpectralFormer, operating at a lower token resolution (128 vs. 512 tokens) and with less than half the number of parameters (1.5M vs. 3.37M). MSM yields consistent gains across both tasks, while PPP further enhances performance for both identification and unmixing when tuned with an appropriate peak prominence. These results highlight multiscale, modality-aware tokenization as an effective and parameter efficient foundation for transformer-based XRF modeling under data-limited conditions. A GitHub repository is provided at https://github.com/sofiane1010/XRFormer.

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