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 progre
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- PossiblePossibly related (embedding) · 45%mlmed/torchxrayvision →
- PossiblePossibly related (embedding) · 45%huggingface/transformers →
- LinkedLinked via arxiv author · 85%Sofiane Daimellah →
“XRFormer: Multiscale Tokenization for XRF Representation Learning”
- LinkedLinked via arxiv author · 85%Sylvie Le Hégarat-Mascle →
“XRFormer: Multiscale Tokenization for XRF Representation Learning”
- LinkedLinked via arxiv author · 85%Clotilde Boust →
“XRFormer: Multiscale Tokenization for XRF Representation Learning”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “XRFormer: Multiscale Tokenization for XRF Representation Lea” ≈ “aymericdamien/TopDeepLearning””